Failure of Administrative Data to Guide Asthma Care
Bibliographic record
Abstract
Rationale: Asthma is a chronic inflammatory disease of the airways that is very common (7.9% ofCanadians over the age of 12). Despite numerous clinical guidelines, education events and administrativedata reviews, there has been little change to the way asthma is managed in the Canadian health caresystem for nearly 30 years. We evaluated, through the Physician Learning Program (PLP) in Alberta,possible reasons why administrative datasets have not been able to provide meaningful information toadjust health policy. Methods: Provincial data was attained through Alberta Health Service and Alberta Health on pulmonaryfunction testing from 2005-2011 (through the PLP). The number of asthma diagnosis made during the sametime frame were then compared. Results: The preliminary results of the PLP found that spirometry was billed for roughly half as often asthe asthma diagnostic codes were utilized during the same time frame. However, the review also revealedinconsistencies in how administrative data are captured, making it difficult to determine whetherspirometry is being underutilized by physicians in making asthma diagnoses. Conclusions: Inconsistencies in how administrative data are captured in Alberta may be contributingto an incomplete picture of the rates of asthma diagnosis and physiological testing, and may explain, inpart, the limited influence of administrative datasets on guiding meaningful change within the healthcaresystem.
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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 | 0.120 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".