Mapping the nomenclature, methodology, and reporting of studies that review methods: a pilot methodological review
Bibliographic record
Abstract
Abstract Background A relatively novel method of appraisal, methodological reviews (MRs) are used to synthesize information on the methods used in health research. There are currently no guidelines available to inform the reporting of MRs. Objectives This pilot review aimed to determine the feasibility of a full review and the need for reporting guidance for methodological reviews. Methods Search strategy: We conducted a search of PubMed, restricted to 2017 to include the most recently published studies, using different search terms often used to describe methodological reviews: “literature survey” OR “meta-epidemiologic* review” OR “meta-epidemiologic* survey” OR “methodologic* review” OR “methodologic* survey” OR “systematic survey.” Data extraction: Study characteristics including country, nomenclature, number of included studies, search strategy, a priori protocol use, and sampling methods were extracted in duplicate and summarized. Outcomes: Primary feasibility outcomes were the sensitivity and specificity of the search terms (criteria for success of feasibility set at sensitivity and specificity of ≥ 70%). Analysis: The estimates are reported as a point estimate (95% confidence interval). Results Two hundred thirty-six articles were retrieved and 31 were included in the final analysis. The most accurate search term was “meta-epidemiological” (sensitivity [Sn] 48.39; 95% CI 31.97–65.16; specificity [Sp] 97.56; 94.42–98.95). The majority of studies were published by authors from Canada ( n = 12, 38.7%), and Japan and USA ( n = 4, 12.9% each). The median (interquartile range [IQR]) number of included studies in the MRs was 77 (13–1127). Reporting of a search strategy was done in most studies ( n = 23, 74.2%). The use of a pre-published protocol ( n = 7, 22.6%) or a justifiable sampling method ( n = 5, 16.1%) occurred rarely. Conclusions Using the MR nomenclature identified, it is feasible to build a comprehensive search strategy and conduct a full review. Given the variation in reporting practices and nomenclature attributed to MRs, there is a need for guidance on standardized and transparent reporting of MRs. Future guideline development would likely include stakeholders from Canada, USA, and Japan.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.619 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.058 | 0.005 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".