UTILIZATION OF VIRTUAL CARE IN POSTOPERATIVE TOTAL KNEE ARTHROPLASTY: ASSESSMENT OF OUTCOME AND PATIENT CHALLENGES
Bibliographic record
Abstract
Virtual physiotherapy has been provided to hundreds of patients at the Holland Centre during the COVID pandemic. As we plan for virtual care to be one part of our care delivery we want to evaluate it and ensure the care delivery is safe and effective. The objectives of this project was two-fold: 1) to examine the outcome of virtual physiotherapy and/ or a hybrid of virtual and in-person care in patients who received post-operative treatment following total knee replacement at the Holland Centre, 2) to explore the challenges of virtual care participation in the joint replacement population. Patients who received either virtual care or a combination of in-person and virtual care (hybrid model) based on the patients’ needs were included. Patient-related outcomes were the Patient Specific Functional Scale (PSFS) and pain scale. Flexion and extension range of motion were measured before and after treatment. A modified Primary Care Patient Experience Virtual Care Survey was used to examine barriers for virtual care. Sixty patients, mean age 68(8), ranging between 45-83 years, 34(57%) females, who received either virtual care or a combination of in-person and virtual care based on the patients’ needs were included. Patients showed improvement in the PSFS and pain scores (p<0.0001). Flexion (p<0.0001) and extension (p=0.02) improved at a statistically significant level. A separate sample (N=54) (age range 50-85 years) completed the patient experience survey. A well-designed post-operative virtual physiotherapy program, initially implemented to maintain continuity of care during the pandemic, continues to be an important part of our model of care as we normalize our activities. Clear understanding of barriers to virtual care and mitigation strategies will help us create virtual care standards, meet our patient needs, optimize our care delivery and potentially increase the use of virtual rehab in the future.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".