Feasibility of Telemedicine in the Management Strategy of Patients With Lymphoma Amid the COVID-19 Pandemic in Spain: Prospective Observational Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».