P.114 The impact of screening for anxious and depressive symptoms on the outcome of patients with a mild traumatic brain injury
Bibliographic record
Abstract
Background: An estimated 27-69 million individuals worldwide sustain a mild Traumatic Brain Injury (mTBI) each year, making it an important public health concern. Many victims experience post-injury neuropsychological issues such as anxiety and depression, which are associated with more post-concussive symptoms and worse functional outcomes. We sought to determine a systematic process to document the presence of anxiety and depression symptoms in mTBI patients to prevent negative impacts on their recovery. Methods: We administered the Generalized Anxiety Disorder-7 (GAD-7) and Center for Epidemiologic Studies Depression Scale Revised (CESDR-10) questionnaires, no more than three months after injury, to screen for these symptoms. A retrospective chart review was performed for 328 patients from the Montreal General Hospital mTBI Clinic who either received these questionnaires ( N =143, M age =40.36, SD age =15.557, N female =90, N male =53) or did not ( N =185, M age =41.17, SD age =16.449, N female =114, N male =71). The number of interventions received between groups were compared using ANOVA. Results: Patients who received the questionnaires ( M =1.34, SD =0.978) were referred to significantly more interventions than those who did not ( M =0.90, SD =0.876, p<0.001) and the rate of referral positively correlated with GAD-7 and CESDR-10 scores. Conclusions: Screening for symptoms of anxiety and depression post mTBI helps clinicians refer patients to the appropriate resources, which in turn should improve outcome.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".