Bibliographic record
Abstract
Background: The prevalence of physician-diagnosed-asthma has risen over the past three decades and misdiagnosis of asthma is potentially common. Objective: to determine whether a secondary-screening-program to establish a correct diagnosis of asthma in those who report a physician diagnosis of asthma is cost effective. Method: Randomly selected physician-diagnosed-asthmatic subjects from 8 Canadian cities were studied with an extensive diagnostic algorithm to rule-in, or rule-out, a correct diagnosis of asthma. Subjects in whom the diagnosis of asthma was excluded were followed up for 6-months and data on asthma medications and heath care utilization was obtained. Economic analysis was performed to estimate the incremental lifetime costs associated with secondary screening of previously diagnosed asthmatic subjects. Analysis was from the perspective of the Canadian healthcare system and is reported in Canadian dollars. Results: Of 540 randomly selected patients with physician diagnosed asthma 150 (28%; 95%CI 19-37%) did not have asthma when objectively studied. 71 % of these misdiagnosed patients were on some asthma medications. Incorporating the incremental cost of secondary-screening for the diagnosis of asthma, we found that the average cost savings per 100 individuals screened was $35,141 (95%CI $4,588-$69,278). Conclusion: Cost savings primarily resulted from lifetime costs of medication use averted in those who had been misdiagnosed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.711 | 0.534 |
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".