[Early rehabilitation after traumatic brain injury and cerebrovascular accident--functional tests database].
Bibliographic record
Abstract
INTRODUCTION: This paper deals with implementation of functional tests used in early rehabilitation into medical record databases. A pilot-testing of certain measurement instruments was performed to establish if they can be used in short intervals as variables for traumatic brain injury patients and those with stroke in intensive care units. The steering group gave critera for inclusion/exclusion. MATERIAL AND METHODS: We followed up four groups of 15 patients. On admission, their Glasgow Coma Score (GCS) was 5-8, 9-12, 13-15. Patients with isolated traumatic brain injury (TBI) were examined using Disability Rating Score (DRS); patients with GCS 13-15 underwent Galveston Orientation and Amnesia Test (GOAT); stroke patients were tested using Motricity Index and the Canadian Stroke Scale. All test were performed for five days in a row. RESULTS: In the first group, DRS results were identical in all cases. Differences were found in patients with GCS higher than eight, while GOAT was positive in four patients that required neuropsychological testing. In stroke patients, Motricity Index was more discriminative than the Canadian Stroke Scale. CONCLUSION: In accordande with our results, the steering group accepted DRS as the variable for patients with GCS 9-12 and 13-15, GOAT for patients with GCS 13-15 and Motricity Index for stroke patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".