Delay of Adequate Empiric Antibiotic Therapy Is Associated with Increased Mortality among Solid-Organ Transplant Patients
Bibliographic record
Abstract
Empiric antibiotic therapy is often prescribed prior to the availability of bacterial culture results. In some cases, the organism isolated may not be susceptible to initial empiric therapy (inadequate empiric therapy or IET). In solid-organ transplant recipients, the overall incidence and clinical importance of IET is unknown. We performed a retrospective cohort study of patients admitted from 2002 to 2004. Multiple logistic regression analyses were conducted to determine associations between potential determinants and mortality. IET was administered in 169/312 (54%) patients, with a hospital mortality rate that was significantly greater than those receiving adequate therapy (24.9% vs. 7.0%; relative risk [RR] 3.55; 95% confidence interval [CI], 1.85-6.83; p < 0.001). Regression analysis demonstrated that an increasing duration of IET (adjusted odds ratio [OR] at 24 h: 1.33; 95% CI: 1.15-1.53; p < 0.001), ICU-associated infections (adjusted OR: 6.27; 95% CI: 2.79-14.09; p < 0.001), prior antibiotic use (adjusted OR: 3.56; 95% CI: 1.51-8.41; p = 0.004) and increasing APACHE-II scores (adjusted OR: 1.26; 95% CI: 1.16-1.34; p < 0.001) were independently correlated with hospital mortality. IET is common and appears to be associated with an increased hospital mortality rate in the solid-organ transplant population.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".