Adaptation and Implementation of a Decision Support Tool For Patient Prioritization Following Mild Traumatic Brain injury : a Study Protocol
Bibliographic record
Abstract
Mild traumatic brain injuries (mTBI) represent 70% to 90% of all TBI cases and their incidence has more than doubled in the past few years. Patients who suffered a mTBI can experience persistent symptoms for a few weeks up to over a year post-injury, which could result in functional disabilities. In Quebec, three months post-injury, patients at risk of chronic symptoms are referred to rehabilitation centers so they can receive specialized services by a multidisciplinary team. However, long waiting lists are common and often make timely access to rehabilitation services impossible. Waiting lists management and patient prioritization are not standardized practice and are often based on opinion of one person. Optimizing waiting list management for rehabilitation services is crucial for mTBI patients and an adapted decision support tool (DST) for patient prioritization could be an interesting solution to help waiting lists management for those patients. The aim of this project is to implement a prioritization DST for mTBI patients in three Quebec trauma rehabilitation programs. To achieve this goal, a survey of stakeholders (patients, clinicians, decision-makers and researchers) will be conducted using the Delphi method until a consensual list of prioritization criteria is obtained. These criteria will be included in the DST-mTBI to facilitate and standardize the assessment of patients' needs. Based on this evaluation, patients will be assigned to a dynamic list according to the consensus-based criteria. The DST-mTBI will then be implemented in three rehabilitation settings in the province of Quebec. Changes induced by this DST-mTBI will be measured on patients (TBI symptoms, anxiety, depression, satisfaction with services) and clinicians (attitudes and sense of clinical utility) using a pre/post design. This project proposes a significant paradigm shift in waiting list management from a time-focused approach to a standardized approach based on potential impacts on individuals.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".