Teleoncology: A Solution for Everyone? A Single-Center Experience with Telemedicine during the Coronavirus Disease 2019 (COVID-19) Pandemic
Bibliographic record
Abstract
Since the beginning of the COVID-19 pandemic, the use of telehealth was rapidly implemented without previous evidence. The ONCOTELEMD study aimed to evaluate the opinion of patients attended via telemedicine during this period and to study factors that condition patient preferences on its use. Included patients had a confirmed cancer diagnosis and were contacted by telephone between 13 March and 30 April 2020, in the Medical Oncology Service of Hospital Parc Taulí, Sabadell. A 12-question survey was presented to them between 4 February and 19 April 2021. Statistical analysis was carried out using chi-square and multivariable logistic regression tests. Six hundred forty-six patients were included; 487 responded to the survey. The median age was 68 years (27–90), 55.2% were female. Most patients had a surveillance visit (65.3%) and were diagnosed with colorectal or breast cancer (43% and 26.5%, respectively); 91.8% of patients were satisfied, and 60% would accept the use of telemedicine beyond the pandemic. Patients aged more than 50 years (OR 0.40; 95% CI, 0.19–0.81; p = 0.01) and diagnosed with breast cancer (OR 0.45; 95% CI, 0.26–0.69; p < 0.001) were less predisposed to adopt telehealth in the future. Patients agreed to be informed via telehealth of scan or lab results (62% and 84%, respectively) but not of new oral or endovenous treatments (52% and 33.5%, respectively). Additionally, 75% of patients had a medium or low-null technologic ability, and 51.3% would only use the telephone or video call to contact health professionals. However, differences were found according to age groups (p < 0.0001). In total, patients surveyed were satisfied with telemedicine and believed telehealth could have a role following the COVID-19 pandemic. Moreover, our results remark on the importance of individualizing the use of telehealth, showing relevant data on patient preferences and digital literacy.
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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 | 0.005 |
| 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".