Risk and distribution of chronic obstructive pulmonary disease–related hospitalizations among primary care patients
Bibliographic record
Abstract
OBJECTIVE: To determine the extent of chronic obstructive pulmonary disease (COPD) hospitalization in easily identifiable high-risk subgroups within a typical primary care practice. DESIGN: Prospective cohort analysis of administrative claims data. SETTING: British Columbia. PARTICIPANTS: British Columbia residents who were 50 years or older on December 31, 2014, and received a physician diagnosis of COPD between 1996 and 2014. MAIN OUTCOME MEASURES: Rate of acute exacerbation of COPD (AECOPD) or pneumonia hospitalization in 2015, broken down by risk identifiers including previous AECOPD admission, 2 or more community respirologist consultations, nursing home residence, or none of these. RESULTS: Of the 242,509 identified COPD patients (12.9% of British Columbia residents ≥50 years), 2.8% were hospitalized for AECOPD in 2015 (0.038 AECOPD hospitalizations per patient-year). The 12.0% with prior AECOPD hospitalization accounted for 57.7% of new AECOPD hospitalizations (0.183 hospitalizations per patient-year); the 7.7% with respirologist involvement accounted for 20.4% (0.102 hospitalizations per patient-year); and the 2.2% in nursing homes accounted for 3.6% (0.061 hospitalizations per patient-year). Those with any of the 3 risk identifiers accounted for only 1.5% more COPD hospitalizations (59.2%) than those with prior AECOPD hospitalization, suggesting prior AECOPD hospitalization is the most important indication of risk. A typical primary care practice held a median of 23 (interquartile range=4 to 65) COPD patients, of whom roughly 20 (86.4%) had none of these risk identifiers. This low-risk majority had only 0.018 AECOPD hospitalizations per patient-year. CONCLUSION: Most AECOPD hospitalizations occur in patients with previous such admissions. When time and resources are limited, COPD initiatives targeting primary care practices should focus more on the 2 to 3 patients with prior AECOPD hospitalization or more symptomatic disease, and less on the low-risk majority.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".