Reduction of the door-to-needle time for administration of antibiotics in patients with a severe infection: a tailored intervention project.
Bibliographic record
Abstract
BACKGROUND: Door-to-needle time (DNT), defined as the time between arrival at the emergency department (ED) and intravenous (iv) antibiotic administration is of crucial importance in the treatment of patients suffering from serious infections. The aim of this project was to reduce the DNT for patients with a serious infection as primary outcome parameter. METHODS: All adult patients arriving at the ED with a suspected infection for whom admission and iv antibiotics were indicated were included. RESULTS: Firstly, baseline DNT was measured and potential delaying factors were identified. Subsequently, five tailored interventions were implemented at regular intervals and their effects on the DNT were analysed. The interventions were: 1) additional resident attendance during peak hours, 2) immediate examination by residents prior to laboratory results, 3) chest X-ray at the ED instead of the external radiology department, 4) iv antibiotic administration at the ED instead of the ward and finally, 5) primary dipstick urine analysis at the ED. A total of 295 patients were included (53.9% men), median age was 59 years (IQR 46 to 73). Median baseline DNT was 183 min (IQR 122 to 296). Implementation of the first three interventions did not reduce the DNT ; however, after implementation of the fourth (administer all antibiotics at the ED) and finally all five interventions the DNT was reduced by 15.3% (p=0.040) to a final median DNT of 155 min (IQR 95 to 221). CONCLUSION: Identification of delaying factors and implementation of tailored interventions reduces the DNT .
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".