External Validation of the Post-Concussion Symptoms Rule for Predicting Mild Traumatic Brain Injury Outcome
Bibliographic record
Abstract
Persistent symptoms are common after a mild traumatic brain injury (mTBI). The Post-Concussion Symptoms (PoCS) Rule is a newly developed clinical decision rule for the prediction of persistent post-concussion symptoms (PPCS) 3 months after an mTBI. The PoCS Rule includes assessment of demographic and clinical characteristics and headache presence in the emergency department (ED), and follow-up assessment of symptoms at 7 days post-injury using two thresholds (lower/higher) for symptom scoring. We examined the PoCS Rule in an independent sample. We analyzed a clinical trial that recruited participants with mTBI from EDs in Greater Vancouver, Canada. The primary analysis used data from 236 participants, who were randomized to a usual care control group, and completed the Rivermead Postconcussion Symptoms Questionnaire at 3 months. The primary outcome was PPCS, as defined by the PoCS authors. We assessed the overall performance of the PoCS rule (area under the receiver operating characteristic curve [AUC]), sensitivity, and specificity. More than 40% of participants (median age 38 years, 59% female) reported PPCS at 3 months. Most participants (88%) were categorized as being at medium risk based on the ED assessment, and a majority were considered as being at high risk according to the final PoCS Rule (81% using a lower threshold and 72% using a higher threshold). The PoCS Rule showed a sensitivity of 93% (95% confidence interval [CI], 88-98; lower threshold) and 85% (95% CI, 78-92; higher threshold), and a specificity of 28% (95% CI, 21-36) and 37% (95% CI, 29-46), respectively. The overall performance was modest (AUC 0.61, 95% CI 0.59, 0.65). In conclusion, the PoCS Rule was sensitive for PPCS, but had a low specificity in our sample. Follow-up assessment of symptoms can improve risk stratification after mTBI.
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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 | 0.058 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".