Patient satisfaction with virtual evaluation, diagnosis, and treatment of CRPS
Bibliographic record
Abstract
Background: The COVID-19 pandemic has led to an increased reliance on virtual care in the rehabilitation setting for patients with conditions such as complex regional pain syndrome (CRPS). Aims: The aim of this study was to perform a quality improvement initiative to assess patient satisfaction and ensure that outcomes following virtual assessment, diagnosis, and treatment of CRPS with prednisone are safe and effective. Methods: An online survey was distributed to 18 patients with CRPS who had been seen virtually between March and December 2020 through a rehabilitation clinic and treated with oral prednisone. Thirteen participants completed the survey, which was designed de novo by our team to evaluate participant perceptions and satisfaction regarding the virtual care experience. Also included in the survey was a CRPS-specific validated patient-report questionnaire (Hamilton Inventory for CRPS: PR-HI-CRPS), which allowed participants to describe their specific symptoms and associated functional and psychosocial impacts, both previously (pretreatment baseline) and at the time of survey (posttreatment). Results: CRPS symptoms and related impacts were scored as significantly improved from baseline following treatment with prednisone. Likert scale results from survey responses related to patients' experiences and satisfaction with the virtual care process were analyzed; the majority of patients reported satisfaction with a virtual appointment for evaluation of CRPS, as well as with subsequent treatment decisions based on virtual assessment. Conclusions: This quality improvement study suggests that virtual care is a potential option for a patient-accepted approach to overcoming challenges with in-person care imposed by the COVID-19 pandemic and could help inform future considerations in addressing geographic and patient-specific disparities in access to specialist care for CRPS.
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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 | 0.005 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".