Antimicrobials in Acute Exacerbations of Chronic Obstructive Pulmonary Disease ‐ An Analysis of the Time to Next Exacerbation Before and After Implementation of Standing Orders
Bibliographic record
Abstract
OBJECTIVE: To compare the mean time to next exacerbation in patients with acute exacerbations of chronic obstructive pulmonary disease (COPD) before and after the implementation of standing orders. SETTING: Tertiary care hospital, Halifax, Nova Scotia, Canada. POPULATION STUDIED: The records of 150 patients were analyzed, 76 were in the preimplementation group, 74 in the postimplementation group. INTERVENTION: The management and outcomes of patients admitted with an acute exacerbation of COPD before and after the implementation of standing orders were compared. DESIGN: A retrospective chart review. MAIN RESULTS: THERE WAS NO DIFFERENCE IN THE MEAN TIME TO NEXT EXACERBATION BETWEEN TREATMENT GROUPS (PREIMPLEMENTATION GROUP: 310 days, postimplementation group: 289 days, P=0.53). Antibiotics were used in 90% of the cases (preimplementation group: 87%, postimplementation group: 93%). The postimplementation group had a 20% increase in the use of first-line agents over the preimplementation group. Overall, first-line agents represented only 37% of the antibiotic courses. CONCLUSIONS: The implementation of standing orders encouraged the use of first-line agents but did not influence subsequent symptom resolution, length of hospital stay, or the infection-free interval in patients with acute exacerbations of COPD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".