Development of an orthopedic surgery trauma patient handover checklist
Bibliographic record
Abstract
BACKGROUND: In surgery, preoperative handover of surgical trauma patients is a process that must be made as safe as possible. We sought to determine vital clinical information to be transferred between patient care teams and to develop a standardized handover checklist. METHODS: We conducted standardized small-group interviews about trauma patient handover. Based on this information, we created a questionnaire to gather perspectives from all Canadian Orthopaedic Association (COA) members about which topics they felt would be most important on a handover checklist. We analyzed the responses to develop a standardized handover checklist. RESULTS: Of the 1106 COA members, 247 responded to the questionnaire. The top 7 topics felt to be most important for achieving patient safety in the handover were comorbidities, diagnosis, readiness for the operating room, stability, associated injuries, history/mechanism of injury and outstanding issues. The expert recommendations were to have handover completed the same way every day, all appropriate radiographs available, adequate time, all appropriate laboratory work and more time to spend with patients with more severe illness. CONCLUSION: Our main recommendations for safe handover are to use standardized checklists specific to the patient and site needs. We provide an example of a standardized checklist that should be used for preoperative handovers. To our knowledge, this is the first checklist for handover developed by a group of experts in orthopedic surgery, which is both manageable in length and simple to use.
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 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 | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".