DELIVERING TAILORED REHABILITATION THROUGH AN ELECTRONIC PATIENT RECORD TO PROMOTE PHYSICAL FUNCTION
Bibliographic record
Abstract
Rehabilitation has potential to exploit technology to address changes in physical functioning associated with chronic diseases and aging. This cohort study was designed to determine feasibility of using an electronic patient health record to prevent the physical functional decline in persons ≥44years with and without chronic diseases/conditions. Participants completed self-report measures including assessments of function and preclinical disability, the Rapid Assessment of Physical Activity (RAPA), at baseline, 6, 12, and 18 months. Participants, 97 persons with chronic diseases/conditions (CD) and 50 persons without (NCD), identified goals using the Patient Specific Functional Scale (PSFS). Using the assessment results, physical and occupational therapists tailored recommendations delivered electronically to address the goals. A library of therapist intervention pages (TIPs) on rehabilitation strategies was created, with topics such as back pain, energy conservation, managing arthritis, balance exercises etc.. Forty-two percent of persons with CD had no difficulty or preclinical changes at baseline compared to 92% without chronic disease; 35% with CD and 8% (NCD) had early changes or difficulty; while 23% (CD) had established difficulty in physical functioning, experiencing significant or longstanding difficulties with physical functioning, mobility, or activities of daily living. Although the range of health-related activities identified with the PSFS varied, functional mobility and exercise/physical activity items were prominent. After 6 months, significant changes in physical activities (RAPA; p=0.05) were detected in the CD group. Findings suggest that on-line monitoring and delivery of rehabilitation strategies support improvements in physical activities and thus promote physical functioning for people with chronic 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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".