Delays From First Medical Contact to Antibiotic Administration for Sepsis*
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between total medical contact, prehospital, and emergency department delays in antibiotic administration and in-hospital mortality among patient encounters with community-acquired sepsis. DESIGN: Retrospective cohort study. SETTING: Nine hospitals served by 21 emergency medical services agencies in southwestern Pennsylvania from 2010 through 2012. PATIENTS: All emergency medical services encounters with community acquired sepsis transported to the hospital. MEASUREMENTS AND MAIN RESULTS: Among 58,934 prehospital encounters, 2,683 had community-acquired sepsis, with an in-hospital mortality of 11%. Median time from first medical contact to antibiotic administration (total medical contact delay) was 4.2 hours (interquartile range, 2.7-8.0 hr), divided into a median prehospital delay of 0.52 hours (interquartile range, 0.40-0.66 hr) and a median emergency department delay of 3.6 hours (interquartile range, 2.1-7.5 hr). In a multivariable analysis controlling for other risk factors, total medical contact delay was associated with increased in-hospital mortality (adjusted odds ratio for death, 1.03 [95% CI, 1.00-1.05] per 1-hr delay; p < 0.01), as was emergency department delay (p = 0.04) but not prehospital delay (p = 0.61). CONCLUSIONS: Both total medical contact and emergency department delay in antibiotic administration are associated with in-hospital mortality in community-acquired sepsis.
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".