The Association of Antibiotic Treatment Regimen and Hospital Mortality in Patients Hospitalized With Legionella Pneumonia
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend azithromycin or a quinolone antibiotic for treatment of Legionella pneumonia. No clinical study has compared these strategies. METHODS: We performed a retrospective cohort analysis of adults hospitalized in the United States with a diagnosis of Legionella pneumonia in the Premier Perspectives database (1 July 2008-30 June 2013). Our primary outcome was hospital mortality; we additionally evaluated hospital length of stay, development of Clostridium difficile colitis, and total hospital cost. We used propensity-based matching to compare patients treated with azithromycin vs a quinolone. All analyses were repeated on a subgroup of more severely ill patients, defined as requiring intensive care unit admission or mechanical ventilation or having a predicted probability of hospital mortality in the top quartile for all patients. RESULTS: Legionella pneumonia was diagnosed in 3152 adults across 437 hospitals. Quinolones alone were used in 28.8%, azithromycin alone was used in 34.0%, and 1.8% received both. Crude hospital mortality was similar: 6.6% (95% confidence interval [CI], 5.0%-8.2%) for quinolones vs 6.4% (95% CI, 5.0%-7.9%) for azithromycin (P = .87); after propensity matching (n = 813 in each group), mortality remained similar (6.3% [95% CI, 4.6%-7.9%] vs 6.5% [95% CI, 4.8%-8.2%], P = .84 for the whole cohort, and 14.9% [95% CI, 10.0%-19.8%] vs 18.3% [95% CI, 13.0%-23.6%], P = .36 for the more severely ill). There was no difference in hospital length of stay, development of C. difficile, or total hospital cost. CONCLUSIONS: Use of azithromycin alone or a quinolone alone for treatment of Legionella pneumonia was associated with similar hospital mortality. Few patients receive combination therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".