Patient Satisfaction with Telephone Follow-up after Lung Resection: Are we making the right ‘call’? Telephone Follow-up after Lung Resection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it