A feasibility assessment of a traumatic brain injury predictive modelling tool at Kilimanjaro Christian Medical Center and Duke University Hospital
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the most common cause of death and disability globally. TBI, which disproportionately affects low middle-income countries (LMIC), uses significant amounts of health system resources in costly care and management. Innovative solutions are required to address this high burden of TBI. One possible solution is prognostic models which enhance diagnostic ability of physicians, thereby helping to tailor treatments more effectively. This study aims to evaluate the feasibility of a TBI prognostic model developed in Tanzania for use by Kilimanjaro Christian Medical Center (KCMC) healthcare providers and Duke-affiliated healthcare providers using human centered design methodology. Duke participants were included to gain insight from a different context with more established practices to inform the TBI tool implementation strategy at KCMC. To evaluate the feasibility of integrating the TBI tool into potential workflows, co-design interviews were conducted with emergency physicians and nursing staff at KCMC and Duke. Qualitatively, the TBI tool was assessed using human centered design (HCD) techniques. Our research design methods were created using the Consolidated Framework for Implementation Research which considers overarching characteristics of successful implementation to contribute to theory development and verification of implementation strategies across multiple contexts. Our knowledge translation method was guided using the knowledge-to-action framework. Of the 21 participants interviewed, 12 were associated with Duke Hospital, and 9 from Kilimanjaro Christian Medical Centre. Emerging from the data were 6 themes that impacted the implementation of the TBI tool: access, barriers, facilitators, use of the TBI tool, outer setting, and inner setting. To our knowledge, this is the first study to investigate the pre-implementation of a sub-Saharan Africa (SSA) data- based TBI prediction tool using human centered design methodology. Findings of this study will aid in determining under what conditions a TBI prognostic model intervention will work at KCMC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".