Implementation of the best practice guidelines on geriatric trauma care: a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: traumatic injuries are increasingly affecting older patients who are prone to more complications and poorer recovery compared to younger patients. Practices of trauma health care providers therefore need to be adapted to meet the needs of geriatric trauma patients. OBJECTIVE: to assess the implementation of the American College of Surgeons best practice guidelines on geriatric trauma management across level I to III Canadian trauma centres. METHODS: 69 decision-makers working in Canadian trauma centres were approached to complete a web-based practice survey. Percentages and means were calculated to describe the level of best practice guideline implementation. RESULTS: 50 decision-makers completed the survey for a response rate of 72%. Specialised geriatric trauma resources were utilised in 37% of centres. Implementation of mechanisms to evaluate common geriatric issues (e.g. frailty, malnutrition and delirium) varied from 28 to 78% and protocols for the optimisation of geriatric care (e.g. Beers criteria to adjust medication, anticoagulant reversal and early mobilisation) from 8 to 56%. Guideline recommendations were more often implemented in level I and level II trauma centres. The adjustment of trauma team activation criteria to the geriatric population and transition of care protocols were more frequently used by level III centres. CONCLUSION: despite the growing number of older patients admitted in Canadian trauma centres annually, the implementation of best practice guidelines on geriatric trauma management is still limited. Prospective multicentre studies are required to develop and evaluate interdisciplinary knowledge translation initiatives that will promote the uptake of guidelines by trauma centres.
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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 | 0.035 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".