In-Clinic versus Hybrid Cancer Rehabilitation Service Delivery during the COVID-19 Pandemic: An Outcome Comparison Study
Bibliographic record
Abstract
Diminished health-related quality of life (HRQOL) is common among cancer survivors but often amendable to rehabilitation. However, few access real-world rehabilitation services. Hybrid delivery modes (using a combination of in-clinic and synchronous telehealth visits) became popular during the COVID-19 pandemic and offer a promising solution to improve access beyond the pandemic. However, it is unclear if hybrid delivery has the same impact on patient-reported outcomes and experiences as standard, in-clinic-only delivery. To fill this gap, we performed a retrospective, observational, comparative outcomes study of real-world electronic medical record (EMR) data collected by a national outpatient rehabilitation provider in 2020–2021. Of the cases meeting the inclusion criteria (N = 2611), 60 were seen to via hybrid delivery. The outcomes evaluated pre and post-rehabilitation included PROMIS® global physical health (GPH), global mental health (GMH), physical function (PF), and the ability to participate in social roles and activities (SRA). The patient experience outcomes included the Net Promoter Survey (NPS®) and the Select Medical Patient-Reported Experience Measure (SM-PREM). A linear and logistic regression was used to examine the between-group differences in the PROMIS and SM-PREM scores while controlling for covariates. The hybrid and in-clinic-only cases improved similarly in all PROMIS outcomes (all p < 0.05). The association between the delivery mode and the likelihood of achieving the minimal important change in the PROMIS outcomes was non-significant (all p > 0.05). No between-group differences were observed in the NPS or SM-PREM scores (all p > 0.05). Although more research is needed, this real-world evidence suggests that hybrid rehabilitation care may be equally beneficial for and acceptable to cancer survivors and supports calls to expand access to and reimbursement for telerehabilitation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".