Mobility related confidence level in chronic stroke patients through task oriented walking intervention
Bibliographic record
Abstract
Introduction Stroke is a neurological insufficiency caused by cerebrovascular injury resulting in major motor deficits in lower limbs. Different therapeutic interventions are being used for stroke rehabilitation. Task oriented walking intervention is a unique approach used for stroke rehabilitation. This research work was planned to study the effects of Task-Oriented walking Interventions on improving the mobility related confidence level in chronic stroke patients. Method This randomized control trial study was conducted on 48 stroke patients in hospital settings. Informed consent was taken from all the study participants before inclusion in the study population and the data from the study subjects was collected through a specially designed questionnaire, 06 minutes-walk test (6MWT), Timed "Up and Go" test (TUG), and Activity-specific Balance Confidence (ABC) Scale. The study population was randomly divided into two groups as experimental group and control group. Experimental group received Task-Oriented Walking Interventions and conventional treatment while control group received conventional interventions only. Results On statistical analysis, significant improvement in mobility related confidence level for all the three scales TUG, ABC and 6MWT was observed on experimental group as compared to the control group after six weeks of interventions. Conclusion The study concluded that task oriented walking interventions is an effective approach for improving mobility related confidence level in post stroke patients. Keywords: Task oriented training, Mobility related confidence, Stroke, Activity-specific Balance Confidence Scale
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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 | 0.000 | 0.001 |
| 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 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".