1099 Enhancing Patient Safety in the General Surgery Department Through Timely Treatment Escalation Plans: A Standard Audit
Bibliographic record
Abstract
Abstract Treatment escalation plans (TEPs) are critical for ensuring patient safety upon admission and during clinical deterioration. It is essential that TEPs are completed promptly after admission to meet established standards. This study aimed to evaluate whether TEP completion times in the general surgery (GS) department of a hospital in central London align with the trust’s standard, which specifies that TEPs should be completed within 36 hours of admission. A standard audit was conducted, comparing the average time to complete TEPs before and after an educational intervention. Two audit cycles were undertaken, including all emergency admissions under GS. The time from admission to TEP completion was measured. After the first cycle, an educational intervention was implemented, consisting of posters distributed to junior doctors encouraging prompt TEP completion. The second cycle assessed the intervention’s impact. In the first cycle, the average time to complete TEPs was 39.6 hours, which decreased to 34.6 hours in the second cycle. Although this reduction did not achieve statistical significance (p > 0.05), the average completion time met the trust’s standard after the intervention. The average age of patients was 61.6 years in the first cycle and 56 years in the second. Additionally, the percentage of patients discharged before TEP completion decreased from 15% to 5%. This study highlights the positive impact of educational interventions on junior doctors, improving adherence to guidelines and enhancing patient safety.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".