COVID-19 in patients with gynecologic cancer: A preliminary report from the COVID-19 and Cancer Consortium (CCC19).
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".