Feasibility of Telemedicine in the Management Strategy of Patients With Lymphoma Amid the COVID-19 Pandemic in Spain: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: On March 14, 2020, a state of alarm was declared in Spain due to the spread of SARS-CoV-2. Beyond this date, COVID-19 in the country changed the practice of oncologic care. OBJECTIVE: Since recurrent hospital visits were a potential risk factor for contagion, the aim of this prospective observational study was to analyze the consequences of the COVID-19 pandemic in the health care of patients with lymphoma. METHODS: All data were obtained from the electronic medical record. Variables such as age, sex, reason of the visit, use of the patient portal, changes in management, enrollment in clinical trials, and COVID-19 infection were recorded. RESULTS: In all, 290 patients visited the lymphoma clinic, totaling 437 appointments. The median age was 66 (range 18-94) years, and 157 (54.1%) patients were male. Of them, 214 (73.8%) patients had only 1 visit to the clinic. Only 23 (7.9%) patients did not have access to the patient portal. Amid the COVID-19 pandemic, 78 (26.9%) patients remained in active treatment, 35 (12.1%) experienced delays in their treatments, and 6 (2.1%) experienced treatment discontinuation. During the follow-up, only 7 (2.4%) patients had a COVID-19 infection (6 cases with confirmed polymerase chain reaction test and 1 case with clinical suspicion). Despite the implementation of telemedicine strategies to avoid visits to the hospital, 66 (22.8%) patients had in-person visits at the lymphoma clinic. Patients who attended in-person consultations were younger than those who preferred telemedicine consultations (62 vs 66 years; P=.10) and had less use of the patient portal (17/224, 7.6% vs 6/66, 9%; P=.10), although these differences did not reach statistical significance. Patients who attended in-person visits were more likely to have had only 1 visit to the hospital (29/66, 43.9% vs 185/224, 82.6%; P<.001). Regarding the reason of in-person consultations, more patients were on active treatment in comparison to those using telemedicine resources (37/66, 56.1% vs 42/224, 18.3%; P<.001). Patients with a preference for telemedicine strategies had more surveillance visits (147/224, 65.6% vs 24/66, 36.4%; P<.001). Regarding treatment modifications, more treatment delays (29/224, 12.9% vs 6/66, 9.1%; P=.10) and more definite treatment discontinuations (6/224, 2.7% vs 0/66, 0%; P=.10) were seen in patients using telemedicine resources when compared to patients attending in-person visits, although these differences did not reach statistical significance. Regarding the type of therapy, patients attending in-person visits were more likely to receive an intravenous treatment rather than those using telemedicine (23/66, 62.2% vs 17/224, 40.5%; P<.001). CONCLUSIONS: Telemedicine such as patient portals are feasible strategies in the management of patients with lymphoma during the COVID-19 pandemic, with a reduction of in-person visits to the hospital and a very low contagion rate.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".