Real world outcomes in cancer patients with COVID-19 infection: Northern Ireland experience.
Bibliographic record
Abstract
Background: Cancer has been assumed to be associated with a high-risk of morbidity and mortality from COVID-19. Protective measures have incorporated modifications in cancer treatments. There are conflicting data about the impact of COVID-19 infection and outcomes in cancer patients. We aim to describe the impact of demographic and clinical characteristics on COVID-19 outcomes in patients with cancer in Northern Ireland reported within the UK Coronavirus Cancer Monitoring Project (UKCCMP). Method: Prospective data collection including demographics, cancer stage and type, treatment and outcomes occurred for all Northern Irish patients enrolled in the UKCCMP. The primary endpoint was all-cause mortality. Descriptive statistics and logistic regression analysis were performed using SPSSv25. Results: Between March 2020 and March 2021, 110 cases were registered. Median age was 63 years (range 27 to 87). Seventy patients (63.6%) were >60 years and 59 (53.8%) were females. Co-morbidities were reported in 83 patients (72.7%). Most patients had metastatic disease (64, 58.2%). Sixty-seven patients (60.9%) received anticancer treatment in the 4 weeks prior to COVID-19 infection. Of those patients, 35 (52.2%) received chemotherapy. Thirty-nine patients (58.2%) continued treatment as planned; 24 (36.9%) stopped treatment due to SARS-CoV-2 infection. The majority of patients were asymptomatic or experienced mild symptoms (67, 60.9%). Fifty-one (46.3%%) were admitted to hospital for COVID-19. Risk of severe/critical COVID-19 disease was significantly associated with age (OR 1.07 [95% CI 1.03-1.11); p=0.004), pre-existing hypertension (OR 3.29 [95% CI 1.42-7.62]; p=0.02) and thoracic primary malignancy (OR 4.41 [95% CI 1.52-12.74]; p=0.042). Twenty-nine patients (26.3%) died of whom 15 (57.7%) died of COVID-19 and 13 (44.8%) died due to cancer. Risk of death was significantly associated with age (OR 1.05 [95% CI 1.01-1.09]; p=0.014), male sex (OR 3.76 [95% CI 1.51-9.34]; p=0.008) and thoracic primary malignancy (OR 5.35 [95% CI 1.88-15.25]; p=0.014). When corrected for age, gender and co-morbidities, chemotherapy within the past 4 weeks was not significantly associated with mortality (OR 0.65 [95% CI 0.20-2.11]; p=0.476). Conclusion: Age and thoracic cancer diagnosis correlated with survival. Comparison of performance during the pandemic with national benchmarks can inform how regional services should be adapted in preparation for future healthcare crises.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".