Bibliographic record
Abstract
Systematic reviews (SRs) are a structured means of knowledge synthesis used by a variety of healthcare practitioners to aid in medical decision making. The SR, if conducted rigorously, is considered to be at the top of the hierarchy for research studies. In addition to synthesizing evidence, SRs identify research priorities, address questions that may not be answerable by individual studies, and identify gaps to be addressed in future primary research. There are several steps that need to be taken when developing SRs to provide the best available evidence-the most essential being the assessment of risk of bias (ROB). Several ROB tools have been developed for use according to study design. Increasingly used is the assessment of certainty of evidence using approaches such as those developed by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group. Whereas ROB is assessed for individual studies, the certainty of evidence is assessed for each critical or important outcome across studies. Analysis can be quantitative (meta-analysis) or qualitative (narrative), with the former intended to develop estimates of the effect measure (ie, the statistic that compares collated data), with confidence limits around that estimate. This review will focus on the steps required to develop SRs, from registration of the review protocol to the conduct, analysis, and reporting, with a focus on the assessment of ROB and certainty of evidence to ensure the development of a methodological and rigorous process.
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 | 0.353 | 0.515 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.020 |
| Bibliometrics | 0.039 | 0.023 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".