Patient Perspectives on Virtual vs In-Person Posttreatment Care for Brain Metastases
Bibliographic record
Abstract
Purpose To explore brain metastasis patients' perspectives on post-treatment care, comparing virtual and in-person visits, and identifying factors shaping those views. Methods A cross-sectional survey assessed patient perspectives on post-treatment care. We offered the survey to English-fluent patients with internet access who received post-treatment care at a Brain Metastases Clinic (n = 140). One hundred twenty-three participants returned the survey and 112 completed at least 80% of it, a criterion for inclusion. Patients received post-treatment follow-up care either virtually, in-person, or both. Non-parametric data were analyzed using Mann-Whitney U and Chi-Square tests, with a modified linear regression model evaluating factors related to visit satisfaction. Our hypothesis was that virtual care would be rated higher based on doctor punctuality, but lower on personal connection, communication, and overall satisfaction. Results Participants who experienced both visit types rated in-person visits higher for personal connection (χ²(df = 1) = 19.703, p < 0.0001), ability to demonstrate physical problems (χ²(df = 1) = 18.778, p < 0.0001), and confidence in addressing health concerns (χ²(df = 1) = 16.941, p < 0.0001). Overall satisfaction did not significantly differ between visit types (U = 3607.5, z = 1.613, p = 0.107). Doctor punctuality (t = -2.328, SE = 0.32, p = 0.025) and communication effectiveness (t = -3.166, SE = 0.608, p = 0.003) were significant correlates to visit satisfaction. Conclusions Similar levels of satisfaction with virtual and in-person visits suggests that virtual care is a viable alternative to in-person visits. Higher ratings of personal connection felt with the physician, ability to demonstrate physical problems, and having health concerns properly addressed, within in-person visits underscore their importance within a healthcare setting. Additionally, doctor punctuality and communication skills are the most significant factors affecting visit satisfaction in this population, highlighting key areas for improvement in healthcare delivery.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".