Prediction of recovery of continuous memory after traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of measures of initial severity, tests of attention, and demographic characteristics to predict recovery of continuous memory for words over a 24-hour period in patients with acute traumatic brain injury. METHODS: Recovery of continuous memory was assessed prospectively in 94 patients with nonpenetrating traumatic brain injury. A classification and regression tree analysis identified a hierarchical subset of variables that may be used as a simple guideline for predicting recovery of continuous memory. Weibull regression models evaluated and compared the predictive ability of multiple variables. RESULTS: Four groups of patients were identified based on measures of severity of injury and demographic characteristics. These four groups had recovery profiles that were more precise than could be obtained by using the Glasgow Coma Scale alone: mild, about 1 week to recovery of continuous memory; moderate, 1 to 4 weeks; severe, 2 to 6 weeks; and extremely severe, 4 to 8 weeks. Regression analysis confirmed that measures of capacity (inherent resources such as indicated by age) and compromise (general functional brain state measured neuropsychologically) improved prediction over models based only on injury severity measures, such as the Glasgow Coma Scale. CONCLUSIONS: Approaches to predicting recovery of continuous memory in the acute period after traumatic brain injury that take into account multiple measures provide a more sensitive predictive index.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".