A pragmatic critical appraisal instrument for search filters: introducing the CADTH CAI
Bibliographic record
Abstract
OBJECTIVE: To identify or develop a critical appraisal instrument (CAI) to aid in the selection of search filters for use in systematic review searching. The CAI is to be used by experienced searchers without specialized training in statistics or search filter design. METHODS: Through extensive searching and consultation, one candidate instrument was identified. Through expert consultation and several rounds of testing, the instrument was extensively revised to become the Canadian Agency for Drugs and Technologies in Health (CADTH) CAI. RESULTS: The CADTH CAI consists of ten questions and can be applied by experienced searchers with a moderate knowledge of search filter methodology. CONCLUSION: The CADTH CAI provides experienced searchers with a means of selecting the search filter that is most methodologically sound.
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 | 0.679 | 0.864 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".