A validation of the post concussion symptom scale in the assessment of complex concussion using cognitive testing and functional MRI
Bibliographic record
Abstract
BACKGROUND: Clinical assessment of cerebral concussion relies on the presence and duration of post concussive symptoms (PCS). Given that these PCS are subjective reports and not always specific to concussion, their usefulness remains to be validated. OBJECTIVE: To evaluate the usefulness of self-reported PCS by means of cognitive tests and functional MRI (fMRI). METHOD: 28 male athletes with and without concussion were grouped according to their PCS score. They were then administered a computerised cognitive test battery and submitted to an fMRI session where cerebral activations associated with verbal and non-verbal working memory tasks were analysed. RESULTS: Behaviourally, response accuracy and speed on the cognitive test battery were comparable for the control and low PCS group. The moderate PCS group showed significantly slower response times than the control group on the matching (p<0.05) and one-back tasks (p<0.05). The functional MRI study showed reduced task related activation patterns in the dorsolateral prefrontal cortex for both low and moderate PCS groups. Activation peaks outside the regions of interest, not seen in the control group, were also noted for both PCS groups. Regression analyses indicated an inverse relationship between PCS scores and performances on several CogSport subtests. Severity of PCS also predicted fMRI blood oxygen level dependent signal changes in cerebral prefrontal regions. CONCLUSION: Self-reported PCS is associated with an ongoing cerebral haemodynamic abnormality as well as with mild cognitive impairment. These results support the use of the PCS scale in the assessment of cerebral concussion and in monitoring recovery.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".