P.016 Early telephone follow-up for traumatic brain injury patients using the Rivermead post-concussion symptoms questionnaire
Bibliographic record
Abstract
Background: Patients who require hospitalization for a mild or moderate traumatic brain injury (TBI) are often discharged home with uncertainty around their full recovery. This study examines the frequency and severity of common post-TBI symptoms, as assessed by the Rivermead Post-Concussion Symptoms Questionnaire (RPCQ). Methods: All adult TBI inpatients discharged home from the Neurosurgery service were interviewed by phone at two weeks by a rehab-based nurse practitioner. RPCQ components (cognitive, emotional, and somatic) were analyzed; findings and management recommendations were communicated to family practitioners and the treating neurosurgeon. Results: In 46 patients, cognitive symptoms were present in 52%, 91% had somatic, and 100% had emotional symptoms. Fatigue was the most common symptom (67%). Double vision was the least common symptom (4%). Recommendations for managing symptoms, return to work, and need for formal clinical assessment were provided for 37% of cases. Conclusions: All patients admitted to neurosurgery with mild or moderate TBI had symptoms at two weeks. The RPCQ is a low-cost structured evaluative tool which highlights needs and provides guidance for patients and care-givers; it also seems effective in identifying those who may require formal clinical assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".