Appropriate antibiotic utilization in seniors prior to hospitalization for community-acquired pneumonia is associated with decreased in-hospital mortality
Bibliographic record
Abstract
BACKGROUND: We analysed the association of mortality and prescription of antibiotics prior to hospitalization for community-acquired pneumonia. METHODS: We used administrative data (hospital abstracts, physician claims, prescriptions) for seniors (age 61 years and over) for Alberta, Canada from 1 April 1994 to 31 March 1999. RESULTS: Hospitalization of 21 191 seniors occurred during the study period. In about 43% of hospitalizations (n = 9034), a physician was consulted prior to hospital admission. Antibiotics were dispensed to 31% of those with a prior physician visit and in about 72%, the antibiotic choice was deemed appropriate. The odds for mortality were significantly decreased in those with prior physician visits (OR = 0.87, P < 0.01), with any antibiotic prescription (OR = 0.66, P < 0.0001), and with an appropriate antibiotic (OR = 0.68, P = 0.03). The choice of an appropriate antibiotic as opposed to an inappropriate antibiotic resulted in a 2.6% absolute and 38% relative mortality reduction. CONCLUSION: Choosing an appropriate outpatient antibiotic in accordance with published expert opinion guidelines compared with inappropriate antibiotic prescriptions decreased hospital mortality in patients subsequently hospitalized for community-acquired pneumonia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".