Priority-based initiative for updating existing evidence-based clinical practice guidelines: the results of two iterations
Bibliographic record
Abstract
OBJECTIVES: New evidence continues to emerge and requires attention after the release of a clinical practice guideline (CPG). The objective of this article is to describe the Document Assessment and Review (DAR) strategy designed to ensue that the CPGs remain current and their quality maintained and to present the results of two iteration of its implementation. STUDY DESIGN AND SETTING: The DAR process involves an annual assessment of our CPGs and a review of documents that require an update search. Two questionnaires are used to conduct the annual assessment and the review. The review involves evidence search, evidence review, and review approval. RESULTS: In 2011, 109 documents were assessed; 22 (20%) were archived, 1 (1%) was deferred for assessment in 2012, 24 (22%) were considered special cases and 62 (57%) needed a new systematic review of the evidence. Of those 62, 19 (31%) were categorized as urgent, 16 (26%) as high, and others as medium or low priority. In 2012, 88 total documents were assessed; 15 (17%) were archived, 32 (36%) deferred, 3 (3%) were considered special cases, and 38 (43%) were prioritized for review. CONCLUSIONS: Assessment and prioritization of existing CPGs are effective ways of ensuring that resources are directed toward the upkeep of those that are relevant and of highest priority.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.539 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".