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Research Article
Revised

Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review

[version 2; peer review: 2 approved, 1 approved with reservations]
PUBLISHED 17 Aug 2018
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Abstract

Background: Forced expiratory volume in one second (FEV1) is an important cystic fibrosis (CF) prognostic marker and an established endpoint for CF clinical trials. FEV1 is also used in observation studies, e.g. to compare different centre’s outcomes. We wished to evaluate whether different methods of processing FEV1 data can impact on centre outcome.
Methods: This is a single-centre retrospective analysis of routinely collected data from 2013-2016 among 208 adults. Year-to-year %FEV1 change was calculated by subtracting best %FEV1 at Year 1 from Year 2 (i.e. negative values indicate fall in %FEV1), and compared using Friedman test. Three methods were used to process %FEV1 data. First, %FEV1 calculated with Knudson equation was extracted directly from spirometer machines. Second, FEV1 volume were extracted then converted to %FEV1 using clean height data and Knudson equation. Third, FEV1 volume were extracted then converted to %FEV1 using clean height data and GLI equation. In addition, year-to-year variation in %FEV1 calculated using GLI equation was adjusted for baseline %FEV1 to understand the impact of case-mix adjustment.
Results: Year-to-year fall in %FEV1 reduced with all three data processing methods but the magnitude of this change differed. Median change in %FEV1 for 2013-2014, 2014-2015 and 2015-2016 was –2.0, –1.0 and 0.0 respectively using %FEV1 in Knudson equation whereas the median change was –1.1, –0.9 and –0.3 respectively using %FEV1 in the GLI equation. A statistically significant p-value (0.016) was only obtained when using %FEV1 in Knudson equation extracted directly from spirometer machines.
Conclusions: Although the trend of reduced year-to-year fall in %FEV1 was robust, different data processing methods yielded varying results when year-to-year variation in %FEV1 was compared using a standard related group non-parametric statistical test. Observational studies with year-to-year variation in %FEV1 as an outcome measure should carefully consider and clearly specify the data processing methods used.

Keywords

Cystic fibrosis, epidemiology, patient outcome assessment, forced expiratory volume

Revised Amendments from Version 1

As recommended by Prof Burgel, we have:

  1. Performed a sensitivity analysis for the results in Table 2 using only adults aged 18 years and above - we have also done the same for the Bland-Altman analyses that were added following suggestion from Prof McKone
  2. Replaced the term "FEV1 decline" with "year-to-year FEV1 variation"

See the authors' detailed response to the review by Edward McKone
See the authors' detailed response to the review by Pierre-Régis Burgel

Introduction

Cystic fibrosis (CF) is a multi-system genetic condition but the two main affected organs are lungs (resulting in recurrent infections and respiratory failure) and gastrointestinal tract (resulting in fat malabsorption and poor growth)1. Median survival has improved to 45 years, in part because of improvement in care quality2. An important quality improvement initiative is benchmarking, which involves identifying high-performing centres and the practices associated with outstanding performance35. Since forced expiratory volume in one second (FEV1) is an important CF prognostic marker69, it is often used as an outcome measure for benchmarking35,10.

Different statistical methods of analysing FEV1 data can yield different results11, but there is scant attention paid to the methods of processing FEV1 data. We previously reported a statistically significant reduction in year-to-year %FEV1 fall for our CF centre from 2013–201612. We now set out to understand the impact of using different FEV1 data processing methods on our CF centre’s outcome.

Methods

This is a single-centre retrospective analysis of routinely collected clinical data from 2013–2016. Regulatory approval for the analysis was obtained from NHS Health Research Authority (IRAS number 210313). All adults with CF diagnosed according to the UK CF Trust criteria aged ≥16 years were included, except those with lung transplantation or on ivacaftor. These treatments have transformative effects on %FEV11315, thus may affect the interpretation of year-to-year variation in %FEV1.

Demographic data (age, gender, genotype, pancreatic status, CF related diabetes, Pseudomonas aeruginosa status), body mass index (BMI) and FEV1 data were collected by two investigators (HZH and RC / HZH and MEG) independently reviewing paper notes and electronic records. Where data from the two investigators differ, the original data from paper notes or electronic records were reviewed to by both investigators to ensure the accuracy of abstracted data. This process ensures the accuracy of abstracted data and helps avoid potential bias from inaccurate or inconsistent data collection16. FEV1 data were processed with three different methods prior to analysis. First, %FEV1 readings (calculated with Knudson equation17 and available in whole numbers) were directly extracted from spirometer machines. Second, FEV1 volumes (in litres, to two decimal places) were extracted and clean height data were used to calculate %FEV1 (as whole numbers) with Knudson equation17. Third, FEV1 volumes (in litres, to two decimal places) were extracted and clean height data were used to calculate %FEV1 with GLI equation18 using an Excel Macro (Microsoft Excel 2013).

Best %FEV1, i.e. the highest %FEV1 reading in a calendar year for each study subject was used for analysis since it is most reflective of the true baseline %FEV119. Year-to-year %FEV1 change was calculated by subtracting best %FEV1 at Year 1 from Year 2 (i.e. negative values indicate fall in %FEV1 and positive values indicate increase in %FEV1). In addition to calculating year-to-year %FEV1 change using three different FEV1 data processing methods, %FEV1 change calculated with GLI equation was also adjusted for baseline %FEV1 using reference values from Epidemiologic Study of CF (ESCF)20. The ESCF study found median %FEV1 change of –3%/year, –2%/year and –0.5%/year for baseline %FEV1 ≥100%, 40–99.9% and <40% respectively20. Adjusted %FEV1 change was calculated by subtracting median ESCF %FEV1 change from actual %FEV1 change. Thus, an adjusted %FEV1 change >0 meant the subject’s year-to-year change in %FEV1 was less than expected (indicating better health outcome) whilst an adjusted %FEV1 change <0 meant the subject’s year-to-year change in %FEV1 was more than expected (indicating worse health outcome).

%FEV1 change from 2013–2014 to 2015–2016 calculated using different FEV1 data processing methods were compared using Friedman test. Bland-Altman analyses21 were also used to compare year-to-year variation in FEV1 as calculated with Knudson equation against year-to-year variation in FEV1 as calculated with GLI equation, to understand the impact of using different reference equations. Analyses were performed using SPSS v24 (IBM Corp) and Prism v7 (GraphPad Software). P-value <0.05 was considered statistically significant.

Results

This analysis included 208 adults, with 147 adults providing data for all four years. Overall, the cohort was ageing but baseline %FEV1 increased from 2014 onwards (see Table 1).

Table 1. Characteristics of study subjects from 2013 to 2016.

2013201420152016
Excluded
     Lung transplantation, n
     On ivacaftor, n
6
7
6
7
9
9
7
13
Included, n166170185186
Age in years, median (IQR)25 (19 – 31)26 (20 – 32)27 (20 – 34)27 (21 – 34)
Female, n (%)76 (45.8)80 (47.1)87 (47.0)90 (48.4)
Genotype status:
     ≥1 unknown mutation(s), n (%)
     ≥1 class IV-V mutation(s), n (%)
     Homozygous class I-III, n (%)
11 (6.6)
26 (15.7)
129 (77.7)
13 (7.6)
29 (17.1)
128 (75.3)
16 (8.6)
36 (19.5)
133 (71.9)
15 (8.1)
34 (18.3)
137 (73.7)
Pancreatic insufficient, n (%)137 (82.5)135 (79.4)142 (76.8)145 (78.0)
CF related diabetes, n (%)39 (23.5)42 (24.7)42 (22.7)54 (29.0)
P. aeruginosa status: §
     No P. aeruginosa, n (%)
     Intermittent P. aeruginosa, n (%)
     Chronic P. aeruginosa, n (%)
60 (36.1)
37 (22.3)
69 (41.6)
57 (33.5)
36 (21.2)
77 (45.3)
74 (40.0)
31 (16.8)
80 (43.2)
78 (41.9)
29 (15.6)
79 (42.5)
BMI, median (IQR)22.3 (19.7 – 24.6)22.7 (20.0 – 25.0)23.0 (20.3 – 26.0)23.2 (20.4 – 26.0)
Best %FEV1, median (IQR)78.7 (54.1 – 92.5)76.6 (54.4 – 89.7)77.8 (60.4 – 89.0)78.5 (58.5 – 89.6)

Genotype status as defined by international consensus22. Homozygous class I-III mutations indicate ‘severe genotype’.

Pancreatic insufficiency was diagnosed by the clinical team on the basis of ≥2 faecal pancreatic elastase levels <200µg/g stool and symptoms consistent with maldigestion and malabsorption, in accordance to the UK Cystic Fibrosis (CF) Trust guideline.

CF related diabetes was diagnosed by the clinical team on the basis of oral glucose tolerance test and continuous subcutaneous glucose monitoring results, in accordance to the UK CF Trust guideline.

§ Pseudomonas aeruginosa status was determined according to the Leeds criteria23.

The %FEV1 increase was in part due to younger adults with higher %FEV1 transitioning from paediatric care because %FEV1 tended to decline from year to year (see Table 2). However, different year-to-year change in %FEV1 results were obtained with different FEV1 data processing methods. There was statistically significant reduction in year-to-year fall in %FEV1 using %FEV1 readings as recorded in spirometer machines (p=0.016). Cleaning of height data and standardisation of %FEV1 calculation with Knudson equation17 did not alter the magnitude of year-to-year variation in %FEV1, but the p-value was no longer statistically significant (p=0.062). The use of GLI equation altered the magnitude of year-to-year variation in %FEV1 although the trend of reduced year-to-year fall in %FEV1 persisted (p=0.135). Adjustment for baseline %FEV1 further increased the p-value (p=0.210).

Table 2. Discrepancies in year-to-year %FEV1 variation with different methods of processing forced expiratory volume in one second (FEV1) data.

Methods of processing FEV1 data:Change in %FEV1, median (IQR)Friedman
test
p-values
2013 to 2014
(n = 158)
2014 to 2015
(n = 162)
2015 to 2016
(n = 176)
(1) %FEV1 (calculated with Knudson equation) extracted from
spirometer machines used for analysis
–2.0 (–6.0 to 1.0)–1.0 (–3.3 to 2.0)0.0 (–3.0 to 2.0)0.016
(2) FEV1 volume (in L) extracted and height data
were cleaned, then %FEV1 calculated using Knudson
equation
–2.0 (–5.0 to 1.0)–1.0 (–4.0 to 1.0)0.0 (–3.8 to 2.0)0.062
(3) FEV1 volume (in L) extracted and height data were cleaned,
then %FEV1 calculated using GLI equation ϕ
–1.1 (–4.6 to 1.5)–0.9 (–3.2 to 1,5)–0.3 (–2.9 to 1.8)0.135
(4) FEV1 volume (in L) extracted and height data were
cleaned, then %FEV1 calculated using GLI equation,
then change %FEV1 adjusted for baseline %FEV1 using
ESCF reference values §
0.7 (–2.4 to 3.6)1.1 (–1.4 to 3.5)1.6 (–1.3 to 3.7)0.210

ESCF - Epidemiologic Study of cystic fibrosis

The vast majority of the %FEV1 data were from spirometer machines at the Sheffield Adult cystic fibrosis (CF) centre, which were calculated with Knudson equation17 in whole numbers. Some %FEV1 data were from spirometer machines at the Pulmonary Function Unit which operationalised the Knudson equation differently; by calculating age to one decimal place to determine the predicted FEV1. These spirometer machines also provided %FEV1 to two decimal places, but this was rounded to whole numbers for the purpose of analysis. These results were presented at the 2017 North American CF Conference and were published as an abstract in Pediatric Pulmonology12.

FEV1 volumes were available in litres to two decimal places from spirometer machines. Height data were also extracted to allow the calculation of predicted FEV1. This led us to uncover the inconsistency recording of height, which affected 30–40% of the study subjects and would have introduced erroneous variability to the %FEV1 because all equations for predicted %FEV1 are dependent on height. Height data were cleaned to weed out error. Where there was uncertainty regarding the height, the higher value was used to obtain a conservative estimate of %FEV1. To replicate calculation process of the spirometer machines at the Sheffield Adult CF centre, age was rounded down to a whole number and predicted FEV1 in volume were calculated to two decimal places using Knudson equation17. This was used to derive the %FEV1, which was then rounded to whole numbers for the purpose of analysis.

ϕ FEV1 and height data were extracted as above. %FEV1 was calculated using the GLI equation18 using an Excel Macro available at the European Respiratory Society website.

§ %FEV1 calculated using the GLI equation18 as described above, then adjusted for baseline %FEV1 as described in the ‘Methods’ section. An adjusted %FEV1 change of >0 meant the subject’s year-to-year fall in %FEV1 was less than expected for his / her baseline %FEV1, indicating better health outcomes.

Similar results were obtained when restricting the analyses to those aged ≥18 years (see Table 3). Bland-Altman analyses comparing year-to-year variation in %FEV1 calculated from clean FEV1 data using Knudson equation17 vs year-to-year variation in %FEV1 calculated from clean FEV1 data using GLI equation18 indicate the tendency for Knudson equation17 to over-estimate the magnitude of year-to-year fall in %FEV1 by a mean difference of 0.1–0.4% (see Figure 1).

Table 3. Discrepancies in year-to-year %FEV1 variation with different methods of processing forced expiratory volume in one second (FEV1) data among adults aged ≥18 years.

Methods of processing FEV1 data:Change in %FEV1, median (IQR)Friedman
test
p-values
2013 to 2014
(n = 147)
2014 to 2015
(n = 157)
2015 to 2016
(n = 172)
(1) %FEV1 (calculated with Knudson equation)
extracted from spirometer machines used for analysis
–2.0 (–6.0 to 1.0)–1.0 (–3.0 to 2.0)0.0 (–3.0 to 2.0)0.016
(2) FEV1 volume (in L) extracted and height data
were cleaned, then %FEV1 calculated using Knudson
equation
–2.0 (–5.0 to 1.0)–1.0 (–4.0 to 1.0)0.0 (–3.8 to 2.0)0.029
(3) FEV1 volume (in L) extracted and height data were
cleaned, then %FEV1 calculated using GLI equation
–1.3 (–4.6 to 1.3)–1.0 (–3.2 to 1.4)–0.3 (–2.9 to 1.8)0.090
(4) FEV1 volume (in L) extracted and height data were
cleaned, then %FEV1calculated using GLI equation,
then change %FEV1 adjusted for baseline %FEV1 using
ESCF reference values
0.5 (–2.4 to 3.3)1.0 (–1.4 to 3.4)1.6 (–1.3 to 3.7)0.149
fe603db9-4c42-40e8-a447-0860bf33c963_figure1.gif

Figure 1. Bland-Altman plots comparing year-to-year variation in %FEV1 as calculated with Knudson equation (i.e. “Method 2” for processing FEV1 data according to Table 2) against year-to-year variation in %FEV1 as calculated with GLI equation (i.e. “Method 3” for processing FEV1 data according to Table 2).

Dataset 1.Dataset 1. Sheffield forced expiratory volume in one second (FEV1) data.
http://dx.doi.org/10.5256/f1000research.14981.d205603

Discussion

We demonstrated that different centre-level year-to-year variation in %FEV1 results were obtained using different FEV1 data processing methods. In particular, year-to-year fall in %FEV1 was smaller in magnitude when %FEV1 was calculated using GLI equation18 instead of Knudson equation17. This is in part due to the demographic of our centre which has a relatively young adult population. A previous study found a near-linear %FEV1 decline from childhood to adulthood with GLI equation, whereas there was accelerated %FEV1 decline during adolescence and young adulthood when %FEV1 was calculated with Knudson equation24. One advantage of using the GLI equation, which is seamless across all ages, is that it improves the interpretation of %FEV1 decline24,25. Another advantage is that %FEV1 decline can be adjusted for baseline %FEV1 using ESCF reference values (since the ESCF values for %FEV1 decline were calculated using the GLI equation20).

The limitation for all single-centre analysis is the potential lack of generalisability. Another limitation of our analysis is that the ESCF reference values used to adjust year-to-year variation in %FEV1 were derived using a cohort from around 15 years ago20, and may not represent the current population. Our results nonetheless highlighted that year-to-year variation in %FEV1 can be extremely sensitive to the FEV1 data processing methods. This is one of the challenges of using year-to-year variation in %FEV1 to infer quality of care. Another challenge is that %FEV1 lacks sensitivity as an outcome measure. A recent sample size estimation using the UK CF registry data suggests that 273 adults per centre are needed to detect a 5% FEV1 difference at the 95% significance level26. The sensitivity of measures used to detect variations in care quality is particularly pertinent to CF because a relatively small population is spread across many centres. Indeed, only 6/28 (21.4%) of all UK adult CF centres have ≥273 adults. That means process measures, e.g. medication adherence, is important to detect variations in quality of CF care. Mant & Hicks previous demonstrated that measuring processes of care proven in randomised controlled trials to reduce death allows detection of meaningful differences in care quality for myocardial infarction with just 75 cases, whereas 8179 cases would be needed if mortality was used as the quality indicator27.

Given the limitations of FEV1 as an outcome measure in CF, results of centre comparisons based on FEV1 data should be carefully interpreted. Observational studies with year-to-year variation in %FEV1 as an outcome measure should carefully consider and clearly specify the data processing methods used.

Ethical considerations

Regulatory approval for the analysis was obtained from NHS Health Research Authority (IRAS number 210313).

Data availability

Dataset 1: Sheffield forced expiratory volume in one second (FEV1) data 10.5256/f1000research.14981.d20560328

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Hoo ZH, El-Gheryani MSA, Curley R and Wildman MJ. Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research 2018, 7:691 (https://doi.org/10.12688/f1000research.14981.2)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 2
VERSION 2
PUBLISHED 17 Aug 2018
Revised
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Reviewer Report 08 Nov 2018
Clive Osmond, MRC Lifecourse Epidemiology Unit, University ofSouthampton, Southampton, UK 
Approved with Reservations
VIEWS 12
Thank you for sending me this interesting note.  A few thoughts on the analysis from a statistician
  1. It’s an interesting, though sobering, fact that between 30 and 40 percent of the machine-entered heights are incorrect. Normally
... Continue reading
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Osmond C. Reviewer Report For: Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research 2018, 7:691 (https://doi.org/10.5256/f1000research.17436.r39259)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Reviewer Report 31 Oct 2018
Pierre-Régis Burgel, Pulmonary Department and Adult CF CentreGroupe Hospitalier Cochin-Hotel Dieu, Paris Descartes University, Paris, France 
Approved
VIEWS 6
No ... Continue reading
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Burgel PR. Reviewer Report For: Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research 2018, 7:691 (https://doi.org/10.5256/f1000research.17436.r37306)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 1
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PUBLISHED 01 Jun 2018
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Reviewer Report 17 Jul 2018
Pierre-Régis Burgel, Pulmonary Department and Adult CF CentreGroupe Hospitalier Cochin-Hotel Dieu, Paris Descartes University, Paris, France 
Approved with Reservations
VIEWS 16
The authors performed a retrospective analysis of FEV1% predicted data over 3 years in an adult CF center in the UK. They examined FEV1 decline from year to year by calculating variation in best FEV1 during two consecutive years and ... Continue reading
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Burgel PR. Reviewer Report For: Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research 2018, 7:691 (https://doi.org/10.5256/f1000research.16309.r34826)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 17 Aug 2018
    Zhe Hui Hoo, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, S1 4DP, UK
    17 Aug 2018
    Author Response
    We thank Prof Burgel for the review and we will iterate the manuscript taking into account the two very useful suggestions, i.e.
    1. we will perform a sensitivity analysis for ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 17 Aug 2018
    Zhe Hui Hoo, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, S1 4DP, UK
    17 Aug 2018
    Author Response
    We thank Prof Burgel for the review and we will iterate the manuscript taking into account the two very useful suggestions, i.e.
    1. we will perform a sensitivity analysis for ... Continue reading
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Reviewer Report 06 Jul 2018
Edward McKone, Department of Respiratory Medicine,  St Vincent’s Hospital, Dublin, Ireland 
Approved
VIEWS 14
FEV1 as a percent of predicted is widely used as an outcome measure in patients with cystic fibrosis and is one of the metrics used to compare centres or countries in benchmarking exercises.  This manuscript presents data showing that differences ... Continue reading
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McKone E. Reviewer Report For: Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research 2018, 7:691 (https://doi.org/10.5256/f1000research.16309.r34828)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 17 Aug 2018
    Zhe Hui Hoo, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, S1 4DP, UK
    17 Aug 2018
    Author Response
    We thank Prof McKone for the review and we will iterate the manuscript taking into account the suggestion to compare the different reference equations (Knudson vs GLI) using Bland-Altman analysis.

    ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 17 Aug 2018
    Zhe Hui Hoo, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, S1 4DP, UK
    17 Aug 2018
    Author Response
    We thank Prof McKone for the review and we will iterate the manuscript taking into account the suggestion to compare the different reference equations (Knudson vs GLI) using Bland-Altman analysis.

    ... Continue reading

Comments on this article Comments (0)

Version 2
VERSION 2 PUBLISHED 01 Jun 2018
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Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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