81 * GAIT VELOCITY IN A COGNITIVELY IMPAIRED POPULATION DURING AN ACUTE HOSPITAL ADMISSION
Bibliographic record
Abstract
Introduction: Gait velocity (Gvel) is predictive of survival in adults, reflective of health and functional status and assists treatment planning for clinicians (Studenski et al, 2011 JAMA). Gvel has been shown to improve during short inpatient episodes (Braden et al 2012 JGPT) however research designs often exclude patients with cognitive impairment (CI). Our aims were to determine whether Gvel measurement is feasible in patients with CI admitted acutely to an OPU and to identify differences in GVel between patients with and without CI. Method: We retrospectively reviewed the records of patients (n = 103) admitted to our 84 bedded unit between July and August 2012. Specifically we captured episode cognitive status, length of stay (LOS) and 6m GVel (admission and discharge). Cut-offs of cognitive measures enabled division of our sample into those with and without CI. Results: Complete data was captured for 63 (61%) patients (Table 1). 49,(78%) patients were interpreted to be experiencing CI. Clinicians reported no meaningful time difference or adverse incidents when measuring Gvel in cognitively impaired individuals. There were no significant differences between groups in Gvel performances or change, nor in age, LOS or acuity (Table 2). Mean Gvel increased significantly within groups between admission and discharge (Table 3) although of doubtful clinical meaning (<0.1m.sec−1). Mean Gvel at discharge remained consistently slow (≤0.4m.sec−1). General Characteristics Values: mean (SD) unless otherwise stated Comparisons between groups *2 tailed Mann-Whitney U Test Comparison within groups * 2 tailed paired t-test. Conclusions: It was feasible to measure GVel using existing protocols with CI patients. Our patients are discharged with velocities slower than 0.6m.sec−1 which is considered abnormally slow and is associated with declines in functional independence. This has implications for rehabilitation in the community. Literature suggests a meaningful change in GVel of 0.1m.sec−1, which was not achieved in either group. However velocity improvements were higher in the non-impaired group. This might have implications for more tailored physical therapy for patients with CI and warrants further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".