Construction of support content for treatment decision⁃making in patients with refractory allergic rhinitis based on the Ottawa decision support framework
Bibliographic record
Abstract
ObjectiveTo construct the support content of treatment decision⁃making for patients with refractory allergic rhinitis（RAR） and to provide structured guidance for patients' treatment decision⁃making.MethodsUnder the guidance of The Ottawa decision support framework（ODSF），semi⁃structured interviews and literature review were combined to form the first draft of decision support content.The Delphi method conducted two rounds of expert consultations with 15 experts in ear，nose，and throat（ENT） related fields to form the final draft of decision⁃making assistance.ResultsThe effective recovery rates of the two rounds of expert consultations were 88% and 100%，respectively.The authoritative coefficients of the two rounds of experts were 0.877 and 0.897，respectively.The coordination coefficients of first⁃level items，second⁃level items and third⁃level items in the second round of expert opinions were 0.533，0.395 and 0.210，respectively.The final treatment decision⁃making support content for patients with RAR included 3 first⁃level items，6 second⁃level items，and 33 third⁃level items.ConclusionsThe support content of treatment decision⁃making for patients with RAR is reliable and reasonable，which could provide structural guidance for patients' treatment decision⁃making and improve patients' satisfaction with 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 | 0.048 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".