Spectral power, source localization and microstates to quantify chronic deficits from ‘mild’ closed head injury: Correlation with classic neuropsychological tests
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To explore the quantitative relationship between neuropsychological impairment and spectral QEEG power, source localization (s-LORETA) and microstate duration in patients who 'fail to adapt' years after mild closed head injury. METHODS AND PROCEDURES: Differences in classic psychometric measures, QEEG measures, s-LORETA indicators and microstate durations were compared for three levels of neuropsychological impairment ∼(average) 6 years after injury. RESULTS: Patients who displayed the moderate-to-severe neuropsychological impairments typical of mild TBIs exhibited shorter microstates, less power within the alpha band and lower power within the theta and delta bands within caudal regions. There were conspicuous differences in the configurations of microstates, their source localizations and frequency bands. CONCLUSIONS: The systematic relationship between neuropsychological impairment as inferred by classic psychometric measures that can require tens of hours to collect and modern configurational analyses of brain activity that require ∼30 minutes suggests that the latter might be useful in discerning the area of dysfunction and potential focus of treatment for patients with closed head injury years after the event.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".