Geriatric Trauma – A Rising Tide. Assessing Patient Safety Challenges in a Vulnerable Population Using Norwegian Trauma Registry Data and Focus Group Interviews: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Elderly trauma patients constitute a vulnerable group, with a substantial risk of morbidity and mortality even after low-energy falls. As the world's elderly population continues to increase, the number of elderly trauma patients is expected to increase. Limited data are available about the possible patient safety challenges that elderly trauma patients face. The outcomes and characteristics of the Norwegian geriatric trauma population are not described on a national level. OBJECTIVE: The aim of this project is to investigate whether patient safety challenges exist for geriatric trauma patients in Norway. An important objective of the study is to identify risk areas that will facilitate further work to safeguard and promote quality and safety in the Norwegian trauma system. METHODS: This is a population-based mixed methods project divided into 4 parts: 3 quantitative retrospective cohort studies and 1 qualitative interview study. The quantitative studies will compare adult (aged 16-64 years) and elderly (aged ≥65 years) trauma patients captured in the Norwegian Trauma Registry (NTR) with a date of injury from January 1, 2015, to December 31, 2018. Descriptive statistics and relevant statistical methods to compare groups will be applied. The qualitative study will comprise focus group interviews with doctors responsible for trauma care, and data will be analyzed using a thematic analysis to identify important themes. RESULTS: The project received funding in January 2019 and was approved by the Oslo University Hospital data protection officer (No. 19/16593). Registry data have been extracted for 33,344 patients, and the analysis of these data has begun. Focus group interviews will be conducted from spring 2020. Results from this project are expected to be ready for publication from fall 2020. CONCLUSIONS: By combining data from the NTR with interviews with doctors responsible for treatment and transfer of elderly trauma patients, we will provide increased knowledge about trauma in Norwegian geriatric patients on a national level that will form the basis for further research aiming at developing interventions that hopefully will make the trauma system better equipped to manage the rising tide of geriatric trauma. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/15722.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".