Correlations and contribution of neurological and motor variables with degree of autonomy and quality of life in acute stroke patients
Bibliographic record
Abstract
Introduction Because of the increasing demand for physical therapy for acute stroke patients, early prognostic prediction is needed to facilitate clinical management. Objectives 1) To assess the relationship between clinical and self-reported variables regarding the patient's clinical status. 2) To identify the predictive power of clinical variables on self-reported outcome in terms of autonomy and quality of life. Material and methods Analytic cross-sectional study of 50 patients hospitalised with acute stroke. Baseline measurements consisted of clinical variables (Canadian Neurological Scale [CNS], Trunk Control Test [TCT], Motricity Index [MI] of lower limb [MI-LL] and upper limb [MI-UL]) and self-reported outcome variables (Barthel Index [BI], Stroke Impact Scale 16 [SIS-16], Modified Rankin Scale, Multidimensional Scale of Perceived Social Support and Stroke-Specific Quality of Life Scale-38 [ECVI-38, Spanish initials]). Results All the clinical variables were significantly correlated with each other, as were most of the self-reported variables. The clinical variables were significantly correlated with some of the self-reported variables (ranging from r = –.676 [P < .01] to r = .286 [P < .05]). Multivariate analysis provided two prediction models: 1) TCT and CNS accounted for 61% of the variance in the BI, with a greater contribution being made by TCT (β = .440) than CNS (β = .260). 2) TCT and MI-LL accounted for 73% of the variance of ECVI-38 in the General Health Status domain, with MI contributing more (β = –.479) than TCT (β = –.352). Conclusions CNS, TCT and MI-LL may be useful to predict health status and autonomy in acute 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 | 0.001 | 0.004 |
| 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 | 0.001 | 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 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".