Mathematical models of cognitive recovery
Bibliographic record
Abstract
Longitudinal psychological test results are used as dependent variables to explore the complex relationship between length of coma, time of testing on the recovery curve, and corresponding cognitive status after traumatic brain injury (TBI). A database containing 319 TBI patients with a broad spectrum of coma duration was used. Statistical analysis of mixed effects modelling was applied to longitudinal WAIS-R (Wechsler Adult Intelligence Scale-Revised) scores to construct two mathematical models (verbal IQ and performance IQ). The models predict the course of recovery (initial cognitive level post-coma, eventual recovery level, and level of cognitive functioning at any point on the recovery curve) when the duration of coma is known. Performance IQ was found to recover at a rate that is almost four times slower than verbal IQ. The results have important clinical rehabilitation implications. This statistical modelling technique also enables the medical researcher to investigate disease progression or recovery using structured assessments, which would normally be part of the routine medical monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".