COVID-19 in patients with gynecologic cancer: A preliminary report from the COVID-19 and Cancer Consortium (CCC19).
Notice bibliographique
Résumé
5508 Background: Limited information exists regarding the severity of short-term outcomes among patients with gynecologic cancer who are infected with SARS-CoV-2. Methods: Patients with gynecologic cancer and laboratory confirmed SARS-CoV-2 infection were identified from the international CCC19 registry. We estimated odds ratios (OR) from ordinal logistic regression for associations with severity of COVID-19 outcomes, defined from least to most severe as hospitalization, intensive care unit (ICU) admittance, mechanical ventilation, and 30-day mortality. Results: Of 842 patients identified, 48% had endometrial cancer, 24% had ovarian cancer, 22% had cervical cancer, and 6% had dual primary/other gynecologic cancers. The majority were from the United States (86%), most were non-Hispanic White (46%), and the median age was 62 years (IQR 52-72). The majority were diagnosed with localized disease (68%); only 18 (2%) and 15 (2%) were fully or partially vaccinated, respectively. In the 3 months prior to COVID-19, 36% had any cancer treatment, with chemotherapy the most common (23%). When diagnosed with COVID-19, most patients were in remission (50%), while 37% had active disease, including 22% with metastatic disease. Most patients presented with typical COVID-19 symptoms (76%); few had a poor ECOG performance status (PS ≥2, 14%). Outcomes included hospitalization (50%), ICU admittance (12%), mechanical ventilation (8%), and death within 30 days of testing positive for SARS-CoV-2 (10%). In unadjusted models, increasing age (OR: 1.03 1.02-1.04) and Black race (OR 1.91, 1.31-2.77) were associated with increased severity of COVID-19 outcomes. Compared to patients in remission for ≥5 years, those with progressive disease had increased severity (OR 1.88, 1.25-2.82), while those in remission for < 5 years or with stable disease had decreased severity of COVID-19 outcomes (OR 0.55, 0.39-0.76). In multivariable models that included adjustment for age, race, and cancer status, additional factors associated with increased COVID-19 outcome severity included cardiac (OR 1.57, 1.13-2.19) and renal (OR 2.00, 1.33-3.00) comorbidities, an ECOG PS ≥2 (OR 5.15, 3.21-8.27), having pneumonia or pneumonitis (OR 4.08, 2.94-5.66), venous thromboembolism (OR 4.67, 2.49-8.75), sepsis (OR 14.2, 9.05-22.1), or a co-infection within ±2 weeks of SARS-CoV-2 (OR: 4.40, 2.91-6.65); asymptomatic SARS-CoV-2 infection was associated with decreased severity of outcomes (OR: 0.25, 0.16-0.38). The overall case fatality rate was 15.7%. Conclusions: Patients with gynecologic cancer experience significant morbidity and mortality related to infection with SARS-CoV-2. Age, race, cancer status, co-morbidities, and COVID-19 complications were associated with more severe COVID-19 outcomes, along the continuum from least to most, of hospitalization, ICU admittance, mechanical ventilation, and 30-day mortality.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».