023 Feasibility and Efficiency of Strategies For Updating Clinical Practice Guidelines
Bibliographic record
Abstract
Background Exhaustive search strategies (ESS) for updating clinical practice guidelines (CPGs) recommendations are laborious and expensive. Highly sensitive and specific alternative search strategies are necessary to improve the efficiency in recommendations updating. Objectives To compare alternative search strategies against ESS Methods We ran three different search strategies in a convenience sample of four CPGs from the CPGs National Programme in Spain: 1) Original ESS (gold standard); 2) Search strategy in the McMaster Premium LiteratUre Service (PLUS) database; and 3) Restrictive strategy with the least number of MeSH terms and text words from the original ESS. We retrieved the key references (which triggered an update) from the original ESS and evaluated their presence in the PLUS and restrictive strategies results. We calculated the sensitivity, specificity, precision, and accuracy for the PLUS and restrictive strategies compared to the ESS. Results The overall number of references in the PLUS strategy was lower than in the ESS (39,133 versus 2,635). The PLUS strategy retrieved a range of 1.12% to 12.1% of the total number of references retrieved by the ESS per guideline. Discussion Our project assessed two novel restrictive search strategies for the updating of CPGs, which could reduce the workload while displaying similar results. Full final findings of this project will be presented at the GIN meeting. Implications for Guideline Developers/Users Our project has important implications for updating CPGs, informing on the feasibility and efficiency of two novel search strategies.
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 | 0.250 | 0.645 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| 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".