346. IMPACT OF VIRTUAL POST-OPERATIVE CARE ON ESOPHAGECTOMY PATIENTS DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Background The SARS-CoV-2 pandemic has resulted in disruptions to oncology practices amongst healthcare institutions globally. To adapt, telehealth has been increasingly implemented into oncologic care, including that of esophageal cancer patients, which has been met with overall success. However, there remains a lack of evidence in the literature pertaining to the outcomes of virtual postoperative appointments for esophagectomy patients. Methods We conducted a retrospective cohort study to assess the clinical outcomes of esophageal cancer patients attending phone call follow-up (virtual) visits compared to standard in person care after esophagectomy. Demographic data, clinical and disease characteristics, and hospital visit data within 6 months of operation were collected. This included surgical clinic visits, endoscopies, and emergency room (ER) admissions. Results 168 esophagectomy patients underwent follow-up care between March 2020 to May 2022; 70 virtual and 98 in-person. Patients attending virtual appointments had significantly fewer ED admissions (−0.48, p = 0.005*, 95%CI [−0.80, −0.14]). Number of endoscopy visits (0.36, p = 0.110, 95%CI [−0.08, 0980]) and the number of clinic visits (0.42, p = 0.118, 95%CI [−0.11, 0.93]) were similar between the two cohorts. ER visits for virtual cohort included feeding tube complications (20.0%), dysphagia (20.0%), and abdominal pain (13.3%). For the in-person cohort, feeding tube complications (12.8%) and chest pain or cardiac features (9.3%) were the most common reasons. Conclusion The results of this study provide evidence for the increased use of telehealth following esophagectomy. The reduction in emergency admissions in the virtual cohort may be due to increased comfort with phone call appointments or increased fear of hospital visitation during the 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".