Nurse Practitioner-Based Sign-Out System to Facilitate Patient Communication on a Neurosurgical Service
Bibliographic record
Abstract
Failure to communicate important patient information between physicians causes medical errors and adverse patient events. On-call neurosurgery physicians at the Toronto Western Hospital do not know the medical details of all the patients that they are covering at night because they do not care for the entire service of patients during the day. Because there is no formal handover system to transfer patient information to the on-call physician, a nurse practitioner-based sign-out system was recently introduced. Its effectiveness for communication was evaluated with preintervention-postintervention questionnaires and by recording daily logins. There was a statistically significant decrease in number of logins after 8 weeks of use (p = .05, Fisher's exact test), and the tool was abandoned after 16 weeks. Modifications identified to improve the system include the ability to sort by attending physician and to automatically populate the list with new patients. Effective communication is important for reducing medical errors, and perhaps these modifications will facilitate this important endeavor.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".