MetaLLMReporter: An R Shiny App Integrating Meta-Analysis Execution with LLM-Assisted Reporting

Summary
The article presents MetaLLMReporter, an R Shiny application that runs standard meta-analysis for continuous outcomes and then generates draft narrative reports using a large language model (LLM). The app reads a user CSV to calculate aggregated means, standard deviations, and sample sizes, fits fixed-effect or random-effects models using established R packages, prints textual outputs for common diagnostics such as heterogeneity, leave-one-out analysis, publication bias tests, trim-and-fill, meta-regression, subgroup and cumulative analyses, and an optional Bayesian model. It then sends the captured text to an LLM to produce prose in several editorial styles. The paper provides a public GitHub repository and a Zenodo archive.

Rationale
The motivation is clear: investigators often have many numerical outputs from a meta-analysis and need coherent text for reports or manuscripts. The paper frames the tool as a bridge between analytic output and narrative reporting.

To strengthen the motivation further, the authors can add one concrete use case that maps each displayed analysis block to a common reporting requirement, and a short note that LLM text must be checked by a human.

Technical soundness of the description
The top-level architecture is sound and maps well to standard practice in R. However, several details in the current code can yield incorrect or fragile results.

First, cumulative meta-analysis is called without enforcing a chronological or pre-specified ordering. The output will depend on input row order. A meta-analysis usually orders cumulative results by year or another meaningful variable. The app should require a study-level ordering column and sort before calling the cumulative procedure.

Second, if the uploaded CSV lacks the intervention sample size, the server fills one default value from a numeric input and uses it for all studies. This can bias effect size variances and weights. The app should reject inputs that miss required counts for either group or ask for per-study values.

Third, in the meta-regression and Bayesian analysis, the code recomputes study effects with metafor::escalc() using the UI’s effect measure, while the main model uses meta::metacont() with a configurable SMD method. This can produce a mismatch if the SMD correction in the two paths differs. The regression should reuse the study-level effects and standard errors from the fitted meta object or enforce the exact same SMD specification in escalc().

Fourth, the “Study Weights” text pastes names (m$w.random) with the numeric weights. In typical meta::metacont fits the weight vector is not named, so the display can be unlabeled.

Finally, there are plaintext LLM API keys in the code and the key is passed in the URL. Keys must not be hardcoded. They should be read from environment variables or a secrets store and never logged. Also, sampling from multiple keys seems unnecessary.

These issues do not change the overall concept, but they affect correctness, security, and maintainability.

Reproducibility
The article gives repository and archive links, the tool is small, and package names are stated, which makes basic replication feasible.

For stronger reproducibility across systems, the repository should include an renv.lock with exact package versions or a minimal Dockerfile and instructions to run the app. The app should also document the exact CSV schema with a minimal valid example and validation rules, and explain how to set the LLM API key at runtime. Without these pieces, users may encounter version drift or silent data issues.

Interpreting outputs and datasets
The tool prints many standard summaries, but it does not guide users on when a diagnostic is meaningful or how to read it. For example, Egger’s test can be unreliable with few studies or strong heterogeneity; p-curve has assumptions that should be stated; cumulative meta-analysis requires a justified ordering. The app should add short, in-context notes that state applicability conditions and common pitfalls, and it should display a per-study table of computed effects and variances so users can verify inputs. Adding forest and funnel plots would also help users cross-check the prose.

Conclusions supported by findings
It is reasonable to conclude that the app can run standard analyses and produce draft narratives. However, claims about reporting support and workflow gains would be stronger with a small worked example that starts from the sample CSV, shows the key numeric outputs, and compares the LLM drafts with a hand-written baseline. If any performance or quality claim is made, it should be backed with either expert review or a simple user study.

Suggestions to make the article scientifically sound
(i) Enforce a valid ordering for cumulative meta-analysis by requiring an ordering column and sorting before computation.
(ii) Avoid filling in default values and require complete per-study counts, or explicitly document and implement a principled missing-data strategy that does not bias variances.
(iii) Align the meta-regression inputs with the primary model by reusing study-level effects and standard errors from the meta fit or by ensuring escalc() applies the same SMD correction as the main model. 
(iv) Replace the unlabeled weight printout with a labeled table that pairs each studlab with its weight.
(v) Remove hardcoded LLM API keys from the code and read a single key from the environment. Do not pass keys in query strings. Document setup steps for users.
(vi) Add input validation and short interpretive notes next to each diagnostic to warn about small-k limits, heterogeneity effects, and assumptions.
(vii) Provide an renv.lock or Dockerfile and a minimal end-to-end script or vignette that reproduces the example in the article.