Brain Drain: Psychosocial Factors Influence Recovery Following Mild Traumatic Brain Injury—3 Recommendations for Clinicians Assessing Psychosocial Factors
Bibliographic record
Abstract
Synopsis Mild traumatic brain injury is a major global public health concern. While most people recover within days to months, 1 in 5 people with mild traumatic brain injury report persistent, disabling symptoms that interfere with participation in work, school, and sport. People with injuries to regions other than the head may report similar symptoms. The biopsychosocial model of health explains this phenomenon in terms of factors associated with recovery that are not biomedical. Important psychosocial factors include poor recovery expectations and pretraumatic and posttraumatic psychological symptoms. Recent clinical practice guidelines recommend that clinicians examine all relevant biopsychosocial factors that may contribute to persistent postconcussive symptoms and consider them when helping their patients make health-management decisions. However, because clinical training continues to prioritize biomedical symptoms, clinicians may not feel confident in the psychosocial domain. Our objective is to provide 3 recommendations for clinicians to assess psychosocial factors in patients after concussion, and to argue a case for clinicians to improve their skills in assessing psychosocial factors. J Orthop Sports Phys Ther 2019;49(11):842–844. Epub 1 Jun 2019. doi:10.2519/jospt.2019.8849
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".