Abstract PO-026: Minimize impact of pandemic on radiation oncology department: Experience from a moderate-sized regional cancer program in the battle against COVID-19 virus
Notice bibliographique
Résumé
Abstract Introduction: The World Health Organization (WHO) declared COVID-19 virus as pandemic on March 12, 2020. Now it has infected more than 5 million people in 188 countries and caused more than 340,000 deaths. The purpose of this paper is to share the experience of our radiation oncology department in a moderate-sized regional cancer center (1,600 new consults for radiation per year) in the battle against COVID-19, including the safety measures taken and the lessons learned. Methods: Our institution is located in the neighbor city of one of the largest COVID-19 epicenters in the USA. We have taken precautionary measures gradually to continue our practice in radiation oncology in order to reduce impact on vulnerable cancer patients. These include reducing the number of entrance doors for both staff and patients; restricted visitor policy; mandatory screening questionnaires; social distancing in waiting rooms; self-quarantine of staff with travel history or symptoms similar to COVID-19; most responsible physicians reviewing every case to prioritize or defer consultation, investigation, or treatment; telemedicine for most consultation and follow-up visits; universal COVID-19 swabbing test for all symptomatic and asymptomatic cancer patients before starting treatment planning or radiotherapy; full personal protective equipment (PPE) for staff doing CT simulation or delivering treatment; mandatory face mask for everyone in the building; keeping 2/3 of all radiation oncologists (RO) and dosimetrists working from home on a roster schedule; and discouraging handling physical paper charts and documents in a completely paperless working environment. Results: We saw 267 new consults in the 10 weeks between March 16 and May 24, 2020, vs. 274 in the same period last year. There is no significant difference in average consults per RO, 44.5 (30-60) vs. 45.7 (24-67), p=0.799 (Student’s t-test), or wait time within provincial target of 2 weeks, 93.5% vs 97%, p=0.074. We performed 193 swabbing tests for 183 patients, with 10 patients bein.g swabbed twice. Most were asymptomatic (144), with 49 symptomatic. Only 0.52% tested positive (1 asymptomatic case), lower than many other cancer institutions reported in the literature, and there were no cases among staff. During the same 10 weeks, confirmed cases in our community and the province increased from 0 to 912 (6.05% positive tests) and from 142 to 25,904 (4.18% positive tests), with 63 and 2,102 deaths, respectively. Conclusions: Due to restrictions to test asymptomatic patients and to use PPE, the COVID-19 testing rate is far from reaching the provincial target and the new cases and deaths are more than originally predicted. However, our department was not heavily affected due to the diligent team effort ahead of policy changes in the province. It is possible for frontline health care teams to minimize the risk of cancer patients getting COVID-19 and avoid treatment interruptions by planning safety measures early, even before the first case in the community and before formal provincial guidelines become available. Citation Format: Ming Pan, Khalid Hirmiz, Junaid Yousuf, Kitty Huang, Colvin Springer, Ken Schneider, Laura D’Alimonte. Minimize impact of pandemic on radiation oncology department: Experience from a moderate-sized regional cancer program in the battle against COVID-19 virus [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-026.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 ».