Utilisation of search filters in systematic reviews of prognosis questions
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
BACKGROUND: Search filters are designed to increase efficiency of information retrieval and can be particularly useful in limiting the large numbers of articles retrieved for systematic reviews (SRs). Existing published prognosis search filters (or hedges) have lower sensitivity and precision values than their therapy counterparts. OBJECTIVES: Taking into account the relatively poor performance of prognosis filters, this study seeks to identify which methods of limiting search results to prognostic studies are most often used by SR teams. METHODS: One hundred and three SRs of prognostic studies published in 2009 and indexed in MEDLINE were retrieved. Each review's search strategy was reviewed and prognosis-related search terms were extracted. RESULTS: Forty-seven of 103 studies used prognosis-related terms to limit the search. Six SRs of 103 did not specify their search terms, and the remaining 50 SRs used content terms only (no terms related to methodology or prognosis). Of the 47 strategies using prognosis-related terms, only six used a published filter. Many SRs used few or poorly selected prognosis-related search terms which are unlikely to provide the sensitivity generally sought for SRs. CONCLUSIONS: Published prognosis search filters are used in only a small minority of prognosis SRs.
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 | 0.160 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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