Variation in acute exacerbation rates (AER) of COPD over 5 years
Bibliographic record
Abstract
The previous year’s AER is used to grade COPD severity and is felt to be stable year to year. We wished to evaluate AER variation in our practice. Patients with symptoms compatible with COPD, >10 pack-year smoking history, and a post bronchodilator FEV1/FVC 2 days treated with antibiotics ± prednisone defined exacerbation. Spirometry and COPD Assessment Tests were measured when well. Cumulative average AERs were calculated for each patient. Distribution of the 157 subjects by the old GOLD stage was; I 23 (15%), II 89 (57), III 38 (24), IV 7 (4) and new GOLD grade; A 24 (15), B 59 (38), C 7(4), D 67 (43). Figure 1A shows the number of subjects vs. AER by year. The AERs are stable from year to year for the group as a whole. Figure 1B shows the number of subjects vs. AERs by each patient’s cumulative average AER by year. The AERs are stable in the >3 group and there are changes which plateau by 3 years in the >0≤1, >1≤2 and >2≤3 groups. The 0 group declines progressively. ![Figure][1] This small population data demonstrates fluctuation in AERs from year to year in most patients and the cumulative average AER seems to stabilize by 3 years. Those with AER>3 initially continue to experience this high AER. Only 5% of patients remain exacerbation free by the end of 5 years. These longitudinal observations require validation in larger, more regionally divers populations. [1]: pending:yes
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".