Search methods for prognostic factor systematic reviews: a methodologic investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This study retroactively investigated the search used in a 2019 review by Hayden et al., one of the first systematic reviews of prognostic factors that was published in the Cochrane Library. The review was designed to address recognized weaknesses in reviews of prognosis by using multiple supplementary search methods in addition to traditional electronic database searching. METHODS: The authors used four approaches to comprehensively assess aspects of systematic review literature searching for prognostic factor studies: (1) comparison of search recall of broad versus focused electronic search strategies, (2) linking of search methods of origin for eligible studies, (3) analysis of impact of supplementary search methods on meta-analysis conclusions, and (4) analysis of prognosis filter performance. RESULTS: The review's focused electronic search strategy resulted in a 91% reduction in recall, compared to a broader version. Had the team relied on the focused search strategy without using supplementary search methods, they would have missed 23 of 58 eligible studies that were indexed in MEDLINE; additionally, the number of included studies in 2 of the review's primary outcome meta-analyses would have changed. Using a broader strategy without supplementary searches would still have missed 5 studies. The prognosis filter used in the review demonstrated the highest sensitivity of any of the filters tested. CONCLUSIONS: Our study results support recommendations for supplementary search methods made by prominent systematic review methodologists. Leaving out any supplemental search methods would have resulted in missed studies, and these omissions would not have been prevented by using a broader search strategy or any of the other prognosis filters tested.
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.
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 | 0.351 | 0.809 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it