Who to handover: a case–control study of a novel scoring system to prioritise handover of internal medicine inpatients
Bibliographic record
Abstract
BACKGROUND: Handover of patients between care providers is a critical event in patient care. There is, however, little evidence to guide the handover process, including determining which patients to handover. AIM: Compare the ability of gestalt-based handover with two structured scores, the modified early warning score (MEWS) and our novel iHAND clinical decision support system, to predict which patients will be assessed by a physician overnight. METHODS: This case-control study included 90 inpatients, comprising 32 patients assessed overnight (cases) and 58 patients not assessed overnight (controls) at a teaching hospital in British Columbia, Canada (May 2012). Gestalt, MEWS and iHAND scores were analysed against patients seen overnight using logistic regression and receiver-operating characteristic (ROC) curves. RESULTS: Neither current gestalt-based handover practice (odds ratio (OR) 1.50, 95% CI 0.89 to 3.83) nor MEWS (OR 0.96, 95% CI 0.75 to 1.24, area under the ROC curve (AUC) 0.61, 95% CI 0.49 to 0.73) were significantly associated with need to be seen overnight. The iHAND score was associated with need to be seen (OR 1.93, 95% CI 1.24 to 3.02, AUC 0.70, 95% CI 0.60 to 0.81). CONCLUSIONS: The iHAND score had moderate ability to predict which patients required assessment overnight, while MEWS score and current gestalt approach correlated poorly, suggesting the iHAND score may help prioritisation of patients likely to be seen overnight for handover.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".