Exploring the concordance of recommendations across guidelines on chest imaging for the diagnosis and management of COVID-19: A proposed methodological approach based on a case study
Bibliographic record
Abstract
OBJECTIVE: To describe a methodological approach to explore the concordance of recommendations across guidelines and its application to the case of the WHO recommendations on chest imaging for the diagnosis and management of COVID-19. STUDY DESIGN AND SETTING: We followed a methodological approach applied to a case study that included: defining the 'reference guideline' (i.e., the WHO guidance) and the 'reference recommendations'; searching for 'related guidelines' and identifying 'related recommendations'; constructing the PICO for the recommendations; assessing the matching of the PICO of each related recommendation to the PICO corresponding reference recommendation; and assessing the concordance between the PICO-matching recommendations. RESULTS: We identified a total of 89 related recommendations from 22 related guidelines. Out of the 89 related recommendations, 43 partly matched and 1 entirely matched one of the reference recommendations, and out of these, 8 were concordant with one of the reference recommendations. When considering the seven reference recommendations, they had a median of 12 related recommendations (range 3-17), a median of 7 PICO-matching recommendations (range 0-13), and a median of 1 concordant recommendation (range 0-4). CONCLUSION: Following a detailed methodological approach, we were able to explore the concordance between our reference recommendations and related recommendations from other guidelines. A relatively low percentage of recommendations was concordant.
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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 | 0.552 | 0.743 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".