Prognostic factors in stroke rehabilitation: the possible role of pharmacological treatment
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to determine the impact of commonly used and potentially detrimental drugs on rehabilitation results and to clarify their role as prognostic factors. MATERIAL AND METHODS: The study included 154 patients admitted to a rehabilitation hospital for sequelae of a first stroke. Multivariate analyses were performed using effectiveness of treatment, evaluated by both the Barthel Index (BI) and the Rivermead Mobility Index (RMI) and low response on both of these indexes as dependent variables. Independent variables were medical, demographic and pharmacological factors. RESULTS: The use of detrimental drugs was negatively associated with effectiveness on both BI and RMI. Severity of stroke (Canadian Neurological Scale score at admission) and hemineglect were the other negative prognostic factors that significantly entered the analyses. On the other hand, the presence of Broca's aphasia positively influenced the recovery, essentially due to prolonged length of stay. The presence of detrimental drugs and hemineglect were associated with a higher risk of low response on both BI and RMI. CONCLUSION: These findings confirm that the use of some drugs can influence rehabilitation results. Therefore, the choice of pharmacological treatment of stroke patients should be carefully evaluated by considering the potential detrimental effects of some drugs commonly used for the treatment of coincidental medical conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".