Diagnosing Asthma: The Fit between Survey and Administrative Database
Bibliographic record
Abstract
BACKGROUND: Standard methods for population studies of asthma include surveying population samples using questionnaires and examining people in laboratories. These procedures are extremely expensive. It would be helpful if, at least for some purposes, they could be replaced by cheaper techniques with adequate validity. OBJECTIVES: To determine agreement between survey and database in regard to the prevalence of asthma. METHODS: Responses to survey questions about asthma symptoms in the past 12 months were linked to physician claims in the Manitoba Population Health Repository. RESULTS: The overall agreement was moderate (k=0.45 to 0.50) and increased if two years of physician claims were studied (k=0.55 to 0.59); studying additional years had no further effect on agreement. Sex and smoking did not significantly affect the kappa scores. CONCLUSIONS: There were several plausible reasons for discrepancies. Symptoms recorded on the survey were intrinsically different from those recorded for physician visits. Physicians also used other respiratory codes instead of asthma, and survey participants did not see a physician every year for asthma. The estimates of prevalence derived from the survey and the administrative database included two overlapping groups of people. In each, the diagnosis of asthma seems justifiable, although the agreement between the two groups was only moderate to substantial. Both methods are useful, although they are useful for different purposes. Health care utilization estimates may be particularly useful for studying trends over time.
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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 | 0.109 | 0.343 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".