Patient Satisfaction with Telephone Follow-up after Lung Resection: Are we making the right ‘call’? Telephone Follow-up after Lung Resection
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, healthcare institutions increased utilization of telemedicine. The impact of telemedicine on quality of care in a surgical setting is an under researched area of the literature. The purpose of this study was to evaluate patient satisfaction with telephone follow-up after lung resection. Methods: All lung cancer patients undergoing a post-operative telephone follow-up between April to November 2020 who had also previously completed at least one in-person pre-operative visit or follow-up were invited to participate. An anonymous online questionnaire adapted from the Telehealth Useability Questionnaire was circulated to participants. Our study’s primary outcome was patient satisfaction with telephone follow-up, compared with in-person visits before COVID-19. Secondary outcomes included surveying patients’ levels of concern about COVID-19, its perceived impact on their medical care, and their views on the utility of telemedicine post-pandemic. Results: A total of 47 out of 54 patients completed the survey. Regarding COVID-19, 85% (39/46) of respondents were “somewhat” or “very” concerned about the pandemic in general and 76% (34/45) reported similar concerns about in-person healthcare appointments. There was no significant difference in participant comfort level and openness to telephone follow-ups before and after the actual encounter (p = 0.08). There was no significant difference reported between in-person and telephone appointments on all paired satisfaction questions directly comparing the two. Conclusions: Patient satisfaction with telephone follow-up after lung resection appears non-inferior to in-person appointments. The convenience of telemedicine for both patients and physicians may warrant sustained utilization of this modality of care post-pandemic.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".