Identifying Individuals with Physcian Diagnosed COPD in Health Administrative Databases
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
Chronic Obstructive Pulmonary Disease (COPD) is a common chronic respiratory disease responsible for significant morbidity and mortality. Population-based health administrative databases provide a powerful and unbiased way of studying COPD in the population, however, their ability to accurately identify patients with this disease must first be confirmed. The objective was to validate population-based health administrative definitions of COPD. Previously abstracted medical records of adults over the age of 35 randomly selected from primary care practices in Ontario, Canada were reviewed by an expert panel to establish if an individual did or did not have a diagnosis of COPD. These reference designations were then linked to each individual's respective health administrative database record and compared with predefine health administrative data definitions of COPD. Concepts of diagnostic test evaluation were used to calculate and compare their test characteristics. The most sensitive health administrative definition of COPD was 1 or more ambulatory claims and/or 1 or more hospitalizations for COPD that yielded a sensitivity of 85.0% (95% confidence interval 77.0 to 91.0) and a specificity of 78.4% (95% confidence interval 73.6 to 82.7). As number of ambulatory claims in the definition increased, sensitivity decreased and specificity increased. Individuals with COPD can be accurately identified in health administrative data, and therefore it may be used to create an unbiased population cohort for surveillance and research. This offers a powerful means of generating evidence to inform strategies that optimize the prevention and management of COPD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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