The heterogeneity of mild traumatic brain injury: Where do we stand?
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To explore the heterogeneity of mild traumatic brain injury (mTBI). METHODS AND PROCEDURES: Hospital-based prospective follow-up study of 125 patients with mTBI sub-divided into 'severity' sub-groups on the basis of GCS scores (GCS of 15 = mild sub-group; GCS of 13-14 = moderate sub-group). Post-traumatic amnesia (PTA) duration (30 minutes used as a cut-off) was also used to define group membership for secondary analyses. The follow-up assessment consisted of a brief neuropsychological battery as well as measures of neurobehavioural functioning, community integration and post-concussive symptomatology. CT scanning was also obtained when clinically relevant. MAIN OUTCOMES AND RESULTS: The two mTBI sub-groups, as defined by GCS scores, did not differ with respect to post-concussive symptomatology, neurobehavioural symptoms, neuropsychological performance or CT scan abnormalities. In contrast, when group membership was redefined on the basis of PTA, the two sub-groups differed significantly with respect to intracranial abnormalities and report of aggressive or disinhibited behaviours at the 6-month mark. CONCLUSIONS: While the notion of heterogeneity in mTBI was not supported when severity was based on GCS scores, there was partial support when PTA duration was used as a measure of severity.
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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 | 0.021 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".