Development and validation of database indexes of asthma severity and control
Bibliographic record
Abstract
BACKGROUND: The use of administrative databases to perform epidemiological studies in asthma has increased in recent years. The absence of clinical parameters to measure the level of asthma severity and control is a major limitation of database studies. A study was undertaken to develop and validate two database indexes to measure the control and severity of asthma. METHODS: Database indexes of asthma severity and control were derived from definitions in the Canadian Asthma Consensus Guidelines based on dispensed prescriptions and on medical services recorded in two large administrative databases from the Canadian province of Québec (Régie de l'Assurance Maladie du Québec (RAMQ) and MED-ECHO) over 12 months. The database indexes of asthma severity and control were validated against the pulmonary function test results of 71 patients with asthma randomly selected from two asthma clinics, and they were also applied to a cohort of patients with asthma followed up for 139 283 person-years selected from the RAMQ and MED-ECHO databases between 1 January 1997 and 31 December 2004. RESULTS: According to the database indexes, 49.3%, 29.6% and 21.1% of patients recruited at the asthma clinics were found to have mild, moderate and severe asthma, respectively, while 53.5% were found to have controlled asthma. The mean predicted value of the forced expiratory volume in 1 s (FEV(1)) ranged from 89.8% for mild asthma to 61.5% for severe asthma (p<0.001), whereas the range from controlled to uncontrolled asthma was 89.5% to 67.3% (p<0.001). The ratio of the FEV(1) to the forced vital capacity (FEV(1)/FVC ratio) measured in 56 patients ranged from 75.8% for mild asthma to 61.8% for severe asthma (p = 0.030), whereas the range from controlled to uncontrolled asthma was 75.3% to 65.7% (p<0.001). CONCLUSION: In the absence of clinical data, these database indexes could be used in epidemiological studies to assess the severity and control of asthma.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".