Effect of Smartphone App Postoperative Home Monitoring After Oncologic Surgery on Quality of Recovery
Notice bibliographique
Résumé
Importance: There has been an increase in health care-focused smartphone apps, including those for encouraging healthy behaviors and managing chronic conditions, but app-assisted postsurgical care has yet to be fully explored. Objective: To compare quality of recovery and patient satisfaction between conventional in-person follow-up and smartphone app-assisted follow-up for patients following Enhanced Recovery After Surgery Society (ERAS) protocols. Design, Setting, and Participants: This randomized clinical trial, conducted from June 2019 to April 2021, included women older than 18 years undergoing oncologic breast reconstruction or major gynecologic oncology surgery following ERAS protocols with the care of 2 surgeons at an academic tertiary care center. Interventions: Patients were randomized 1:1 to receive smartphone app-assisted follow-up or conventional in-person follow-up. The smartphone group used a surgeon-monitored app to record Quality of Recovery 15 (QoR15) scores, European Organisation for Research and Treatment of Cancer-selected adverse events, drain outputs, and surgical site photographs over 6 weeks. Patient satisfaction scores were assessed using validated Patient Satisfaction Questionnaire III (PSQ-III) subscales at 2 and 6 weeks postoperatively. The conventional follow-up group also completed the QoR15 and PSQ-III questionnaires at these intervals. Main Outcomes and Measures: The primary outcomes were quality of recovery and patient satisfaction, as measured by the QoR15 and PSQ-III, respectively. Secondary outcomes were costs of follow-up; the number of contacts with the medical system, complications, and surgeons' contacts with patients; and surgeons' perceptions of app-assisted care. Results: Of 72 patients included in the trial, 36 underwent breast reconstruction (mean [SD] age, 45.30 [9.13] years) and 36 underwent gynecologic oncology surgery (mean [SD] age, 54.90 [11.18] years). Three patients dropped out (2 who underwent breast reconstruction [1 in the app group, 1 in the control group], 1 who underwent gynecologic oncology surgery [control group]). The app group had significantly higher mean (SD) QoR15 scores than the control group (2 weeks: 127.58 [22.03] vs 117.68 [17.52], P = .02; 6 weeks: 136.64 [17.53] vs 129.76 [16.42], P = .03). Patients were equally satisfied between groups in all subsets of the PSQ-III at these intervals. The mean (SD) number of complications was similar in both groups, and a similar number of surgeon contacts per patient occurred (1.6 [1.2] vs 2.1 [2.0], P = .16). Surgeons appreciated early identification of complications with the app. Conclusions and Relevance: In this randomized clinical trial, postoperative follow-up for patients undergoing breast reconstruction and gynecologic oncology surgery using smartphone app-assisted monitoring led to improved quality of recovery and equal satisfaction with care compared with conventional in-person follow-up. Trial Registration: ClinicalTrials.gov Identifier: NCT03456167.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,005 | 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 ».