The PRISMATIC project: protocol for a research programme on novel methods to improve reporting and peer review of systematic reviews of health evidence
Bibliographic record
Abstract
BACKGROUND: Incomplete reporting about what systematic reviewers did and what they found prevents users of the report from being able to fully interpret the findings and understand the limitations of the underlying evidence. Reporting guidelines such as the PRISMA statement and its extensions are designed to improve reporting. However, there are important inconsistencies across the various PRISMA reporting guidelines, which causes confusion and misinterpretation. Coupled with this, users might need to consult multiple guidelines to gain a full understanding of the guidance. Furthermore, the current passive strategy of implementing PRISMA has not fully brought about needed improvements in the completeness of systematic review reporting. METHODS: The PRISMATIC ('PRISMA, Technology, and Implementation to enhance reporting Completeness') project aims to use novel methods to enable more efficient and effective translation of PRISMA reporting guidelines into practice. We will establish a working group who will develop a unified PRISMA statement that harmonises content across the main PRISMA guideline and several of its extensions. We will then develop a web application that generates a reporting template and checklist customised to the characteristics and methods of a systematic review ('PRISMA-Web app') and conduct a randomised trial to evaluate its impact on authors' reporting. We will also develop a web application that helps peer reviewers appraise systematic review manuscripts ('PRISMA-Peer app') and conduct a diagnostic accuracy study to evaluate its impact on peer reviewers' detection of incomplete reporting. DISCUSSION: We anticipate the novel guidance and web-based apps developed throughout the project will substantively enhance the completeness of reporting of systematic reviews of health evidence, ultimately benefiting users who rely on systematic reviews to inform health care decision-making.
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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: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.402 | 0.577 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.169 | 0.057 |
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".