The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration
Bibliographic record
Abstract
Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safetyof health care interventions accurately and reliably. The clarity and transparency of these reports, however,is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, andother users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—areporting guideline published in 1999—there have been several conceptual, methodological, and practicaladvances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews ofpublished systematic reviews have found that key information about these studies is often poorly reported.Realizing these issues, an international group that included experienced authors and methodologistsdeveloped PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution ofthe original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health careinterventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. Thechecklist includes items deemed essential for transparent reporting of a systematic review. In thisExplanation and Elaboration document, we explain the meaning and rationale for each checklist item. Foreach item, we include an example of good reporting and, where possible, references to relevant empiricalstudies and methodological literature. The PRISMA Statement, this document, and the associated Web site(http://www. prisma-statement.org/) should be helpful resources to improve reporting of systematicreviews and meta-analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.288 | 0.528 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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.
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".