Failure of Administrative Data to Guide Asthma Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it