Impact of the COVID-19 pandemic on primary care access for patients with gastrointestinal malignancies.
Notice bibliographique
Résumé
32 Background: Primary care physicians (PCPs) provide essential support for cancer patients. Both primary and cancer care have been affected by the COVID-19 pandemic. In the US, cancer related encounters and screening decreased over 40% and 80% respectively in January to April 2020 compared to 2019 (London et al JCO Clin Cancer Inform 2020). However, the impact of the pandemic on primary care access for cancer patients remains unclear. Methods: This was a population-based, retrospective cohort study using administrative healthcare databases held at ICES in Ontario, Canada. Patients with a new gastrointestinal (GI) malignancy diagnosed within the year prior to the pandemic, between July 1 and Sept 30, 2019 (COVID-19 cohort), were compared to patients diagnosed in years unaffected by the pandemic, between July 1 – Sept 30, 2018 and July 1 – Sept 30, 2017 (pre-pandemic cohort). Both groups were followed for 12 months after initial cancer diagnosis. In the COVID-19 cohort, this allowed for at least 4 months of follow-up data occurring during the pandemic. The primary outcome was number of in-person and telemedicine visits with a PCP. Secondary outcomes were number of in-person and telemedicine visits with a medical oncologist, number of emergency department (ED) visits, and number of unplanned hospitalizations. Outcomes, reported as number of visits per person-year, were compared between the COVID-19 and pre-pandemic cohorts. Results: 2833 individuals diagnosed with a new GI malignancy in the COVID-19 cohort were compared to 5698 individuals in the pre-pandemic cohort. The number of in-person visits to PCPs per person-year significantly decreased from 7.13 [95% CI 7.05 – 7.20] in the pre-pandemic cohort to 4.75 [4.66 – 4.83] in the COVID-19 cohort. Telemedicine visits to PCPs increased from 0.06 [0.05 – 0.07] to 2.07 [2.01 – 2.12]. Combined in-person and telemedicine visits to PCPs decreased from 7.19 [7.11 – 7.26] to 6.82 [6.71 – 6.92]. In-person visits to medical oncologists decreased from 3.73 [3.68 – 3.79] to 2.87 [2.80 – 2.94], and telemedicine visits increased from 0.10 [0.10 – 0.11] to 0.95 [0.91 – 0.99]. Combined in-person and telemedicine visits to medical oncologists remained stable (3.84 [3.78 – 3.89] vs. 3.82 [3.74 – 3.90]). The number of ED visits per person-year decreased from 1.04 [1.01 – 1.07] in the pre-pandemic cohort to 0.93 [0.89 – 0.97] in the COVID-19 cohort. Unplanned hospitalizations did not show a significant change (0.56 [0.54 – 0.58] vs. 0.53 [0.50 – 0.56]). Conclusions: PCP visits for patients with newly diagnosed GI malignancies overall decreased during the pandemic, with a dramatic shift from in-person to telemedicine visits. Visits to medical oncologists also shifted from in-person to telemedicine, but the overall combined visits remained the same. While the number of ED visits decreased, the shift in ambulatory practices did not seem to impact the number of unplanned hospitalizations.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».