Surgeon agreement at the time of handover, a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Acute Care Surgical Teams are responsible for emergent surgical patients, and as such require regular handover and coordination between different surgeons. Despite the recent emergence of this model of care, minimal research has been conducted on the quality of patient handover and no research has attempted to determine the rate of clinical agreement or disagreement among surgeons participating in these teams. METHODS: A prospective cohort study was carried out with our acute care surgical service at a tertiary care teaching hospital from January 2 to March 31 2012. At the conclusion of the daily morning handover, receiving surgeons were asked to indicate, on provided handover sheets, whether they agreed with the proposed management plan for each patient that was discussed. The specific aspects of care over which they disagreed were also described, and disagreements were classified a priori as major or minor. The primary outcome was the rate of disagreement over the handed over management plan. RESULTS: Six staff surgeons agreed to participate and a total of 417 unique patients were handed over during the study period. For the primary outcome, a total of 41 disagreements were recorded for a disagreement rate of 9.8 %. 15 of the 41 disagreements were classified as major, for a major disagreement rate of 3.6 %. Consultant to consultant disagreements were classified as major disagreements 63 % of the time, whereas consultant to resident disagreements were classified as major 31 % of the time (P = 0.217). On average, the age of patients for which a clinical disagreement occurred were older; 63 vs. 57 (P < 0.05). CONCLUSIONS: Despite the frequency of handovers in clinical practice, little research has been conducted to determine the rate of disagreement over patient management among surgeons participating working in academic centers. This study demonstrated that the rate of clinical disagreement is low among surgeons working in an tertiary care teaching hospital.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".