Bibliographic record
Abstract
The PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) is a new revised guideline for reporting systematic reviews and metaanalyses.Although the primary focus is on reporting of systematic reviews of randomised trials, PRISMA can be also used for reporting of systematic reviews of other types of research, particularly evaluations of interventions.PRISMA supersedes the existing QUOROM Statement and the new PRISMA checklist differs in several respects from the QUOROM checklist.These changes are clearly highlighted in a 'statement' paper published in Annals of Internal Medicine, PLoS Medicine, BMJ, Journal of Clinical Epidemiology, and Open Medicine [1].There is a dedicated website for PRISMA (www.prisma-statement.org); the site provides the history of the guideline development, downloadable 27-item checklist and flow diagram templates, references of all PRISMA papers and the full text of the Explanation and Elaboration paper (2), which explains the meaning and rationale for each checklist item and includes examples of good reporting.
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
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.638 | 0.532 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".