A scoping review of models of care for the management of older trauma patients
Bibliographic record
Abstract
INTRODUCTION: The number of older people hospitalised with major trauma is rapidly increasing. New models of care have emerged, such as co-management, and trauma centres dedicated to delivering geriatric trauma care. The aim of this scoping review was to explore in-hospital models of care for older adults who experience physical trauma. PATIENTS AND METHODS: The search was conducted in accordance with the PRISMA- SC (preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews) reporting guidelines. The National Heart Lung, and Blood Institute (NIH) study quality assessment tool was used to evaluate risk of bias in before and after non-randomised experimental studies. RESULTS: Of 2127 records returned from the database search, 43 papers were included. We identified five types of care models investigated in the reviewed studies: centralised trauma management, consultation services, co-management, patient care protocols, and alert and triage systems. The majority of patients were admitted under a specialised trauma service, intervention teams were for the most part multidisciplinary, and follow-up of patients post-discharge was seldom reported. Consultation services more often had advanced care and discharge planning as treatment objectives. In contrast, patient care protocol and alert systems commonly had management of anticoagulation as a treatment objective. Overall, the impact of the five models of care on patient outcomes was mixed. DISCUSSION: Given the variability in patient characteristics and capabilities of health services, models of care need to be matched to the local profile of older trauma patients. However, some standards should be incorporated into a care model, including identifying goals of care, medication review and follow up post-discharge.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".