Poor Agreement Among Asthma Specialists on the Choice and Timing of Initiation of a Biologic Treatment for Severe Asthma Patients
Bibliographic record
Abstract
BACKGROUND: Because the number of monoclonal antibodies available for severe asthma is growing, specialists currently choose without clear guidelines. Despite increasing knowledge on treatment response to these monoclonal antibodies, making the optimal choice for each individual patient remains a challenge. However, evidence of this daily challenge is lacking. OBJECTIVE: To evaluate interobserver agreement on the choice of biologic therapy in severe asthma patients among severe asthma specialists, based on clinical cases. METHODS: This 2-phase study included a pilot local study and an international validation study. Asthma specialists were presented 7 real-life asthma cases managed with a monoclonal antibody. Based on the clinical information provided in the cases, they were asked whether they would have initiated a monoclonal antibody and, if so, their treatment of choice between (1) omalizumab, (2) mepolizumab, (3) reslizumab, (4) benralizumab, and (5) dupilumab. Interobserver agreement for each question was assessed using Gwet agreement coefficient (AC1). RESULTS: Sixteen physicians from the Province of Quebec (Canada) completed the pilot survey, and 70 physicians from 26 countries completed the international survey. The Gwet AC1 for the decision to initiate a biological therapy was 0.48 in the pilot survey and 0.33 in the international survey. For the choice of therapy, agreement was 0.33 and 0.26, respectively. CONCLUSIONS: The interobserver agreement among asthma specialists in both the decision to initiate a biological treatment in patients with severe asthma and the selection of treatment is weak. These results highlight the need for studies seeking reliable predictors for optimal response to biological therapies.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".