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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".