Priority Clinical Actions for Outpatient Management of Nonhospitalized Traumatic Brain Injury
Bibliographic record
Abstract
Outpatient care following nonhospitalized traumatic brain injury (TBI) is variable, and often sparse. The National Academies of Sciences, Engineering, and Medicine’s 2022 report on Traumatic Brain Injury: A Roadmap for Accelerating Progress highlighted the need to improve the consistency and quality of TBI care in the community. In response, the present study aimed to identify existing evidence-based guidance and specific clinical actions over the days to months following nonhospitalized TBI that should be prioritized for implementation in primary care. In systematic literature searches, 17 clinical practice guidelines met our eligibility criteria and an additional expert consensus statement was considered highly relevant. We extracted 73 topics covered by one or more existing clinical practice guidelines. After removing redundant and out-of-scope topics, those deemed essential (not requiring prioritization), 42 topics were subjected to a prioritization exercise. Experts from the author group ( n = 14), people with lived experience ( n = 112), and clinicians in the community ( n = 99) selected and ranked topics they considered most important. There were areas of agreement (e.g., early education was ranked highly by all groups) and discordance (e.g., people with lived experience perceived diagnostic tests/investigations as more important than the other groups). We synthesized the prioritization survey results into a top-10 list of the highest priority clinical actions. This list will inform implementation efforts aimed at improving post-acute care for nonhospitalized TBI.
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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 | 0.032 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".