Comparación entre un método semiautomático y una búsqueda tradicional para la identificación de estudios que informen recomendaciones de guías de práctica clínica GES: Un estudio metaepidemiológico
Bibliographic record
Abstract
Recently, there has been a surge in technological tools designed to automate tasks across various areas of health sciences, including the identification of evidence used in the development of evidence syntheses that inform clinical practice guideline (CPG) recommendations. Simultaneously, there has been a significant increase in the production of systematic reviews, meaning that much of the relevant evidence is already included in existing reviews. AIM: To compare the performance of the semi-automated Epistemonikos Evidence Matrix tool with that of a traditional manual literature search in identifying studies for the development of clinical practice guidelines. MATERIALS AND METHODS: During the development of three CPGs (focused on HIV/AIDS, pediatric asthma management, and stroke management), we compared studies identified through a traditional search strategy in MEDLINE, Embase, and the Cochrane Library with those found using a strategy based on existing systematic reviews, via the Epistemonikos database. The traditional search employed keyword-based strategies and a specific filter for randomized controlled trials. In contrast, the Epistemonikos-based strategy relied on the semi-automated Evidence Matrix tool, which identifies studies shared across two or more systematic reviews. RESULTS: Across the three guidelines, 8,466 potentially relevant articles were identified using the traditional method, compared to 6,771 using the Epistemonikos-based method. Of these, 155 studies (1.8%) were deemed truly relevant in the traditional search, versus 103 (1.5%) in the Epistemonikos-based approach (p= 0.14). The approach based on existing reviews demonstrated significantly higher precision (94% vs. 78%, p<0.01) but lower sensitivity (58% vs. 88%, p<0.01) compared to the traditional search. CONCLUSIONS: The evidence search strategy based on existing systematic reviews is an efficient and reliable alternative for identifying relevant studies to support evidence-based decision-making.
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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 | 0.408 | 0.690 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.041 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".