Bibliographic record
Abstract
3040covid testing: covid testingFour specific variables have been identified as risk factors for high mortality in a study with 1,029 patients being treated for hematologic malignancies who become infected with SARS-CoV-2. In findings reported at the 2021 Annual Meeting of the American Society of Hematology (ASH), a pre-COVID prognosis of less than 6 months or a decision to defer intensive care unit (ICU) treatment, along with age and gender, were the only factors associated with increased risk of death from COVID-19. Cancer treatment did not add to the mortality risk from infection (Abstract 3040). “There was real fear in our community among patients and caregivers about what the impact of COVID-19 would be on our patients. We were very worried that we would be seeing high mortality. We wondered if it would be safe even to give cancer treatment,” said lead author hematologist Lisa K. Hicks, MD, MSc, FRCPC, from St. Michael's Hospital in Toronto, as well as Associate Professor in the Department of Medicine at the University of Toronto. She said that, since there were very significant competing risks of death in patients with serious blood cancers, they had analyzed data collected through a network of public volunteers—the ASH RC COVID-19 Registry for Hematology—that had been set up to report outcomes of SARS-CoV-2 infection in patients with underlying blood disorders. “What we, and others, have been reporting is that people with blood cancer do appear to be uniquely susceptible to more serious outcomes than those reported in the general population,” she said. They found an overall 17 percent increase in the risk of death in patients with a range of different blood cancers. The study aimed to identify subgroups of patients in the overall cohort who had been especially at risk of poor outcome, Hicks explained. In a multi-variable analysis, they found that age over 60 and male gender were both independently associated with increased risk of dying among patients who had blood cancer and COVID-19. Importantly, two other factors predicted high mortality in this patient group: the predicted prognosis of patients before infection with COVID-19, and whether or not they chose to be supported with intensive care. “In our registry, we ask clinicians to identify for us their sense of the pre-COVID prognosis, and specifically to identify pre-COVID prognosis of less than or equal to 6months,” Hicks noted. These very unwell people were found to be “uniquely susceptible” to poor outcomes with COVID-19. Since many patients with advanced illness exercise their choice not to pursue aggressive care (and opt for a palliative course for their illness), the researchers wanted to assess the impact of deferring treatment in the ICU by patients who had been candidates for it. “Not surprisingly, choosing that pathway is associated with an increased risk of dying,” Hicks said. And she mentioned that, for her, it was important—because she wanted to know whether outcomes were influenced by patient preference. When she was asked about the impact of active cancer treatment on the course of COVID-19 infection, she said they found no effect. The investigators inferred that, even in a setting of COVID-19 infection, treatment for cancer should still be prioritized. “One of the big questions that we had when COVID-19 began—one of the big unknowns—was [if we] could safely treat people?” Hicks stated. In the univariate analysis of the registry data, the researchers found that treatment for cancer during the previous year was a risk for dying with COVID-19. “But when we adjusted for other factors and did a multi-variable analysis, [it] was not a significant correlate with mortality.” However, active cancer treatment was found to be associated with an increased risk of hospitalization. “Patients who had systemic treatment in the previous year did appear to have a higher risk of being hospitalized with COVID-19 once they became infected, but not [of] mortality,” Hicks said. But, she added that, since the data were retrospective and had no longitudinal follow-up, they should be regarded as hypothesis-generating. “But that finding offers, perhaps, a little reassurance that people were able to receive cancer treatment without (at least in our dataset) increased risk of dying from COVID-19.” The researchers found no differences upon multivariable analysis with mortality between the broad groups of hematologic malignancies—acute leukemia, myelodysplasia, chronic lymphocytic leukemia, multiple myeloma, and myeloproliferative neoplasms. “We were not able to tease apart any difference in those groups,” Hicks said. “But when we looked at hospitalization, there was a difference. [Patients with] plasma cell diseases—myeloma and myeloproliferative neoplasms—seem to be doing better, and were less likely to be hospitalized with their COVID-19.” When Hicks was asked about the cancer clinician's scope for modifying practice as a result of the new knowledge, she said that identifying the patients most at risk could help improve outcomes. “What it does for me: It tells me about the patients that I'm going to be most worried about and [whom] I want to be keeping extra close tabs on,” she said. “Age, certainly, is a big one. And perhaps men.” She said that new therapies for COVID-19 might be prioritized for the most at-risk patients who were more likely to benefit from them. Hicks regarded the overall 17 percent risk of dying among patients with blood cancers as providing yet another reason for taking preventive measures and extreme caution to avoid SARS-CoV-2 infection in the general population—including cancer patients. “Prioritizing vaccination, prioritizing boosters, ongoing care with regard to masking, and other preventative mechanisms,” she stated. “Our study adds to a growing body of literature suggesting that patients with blood cancer who become infected with COVID-19 are likely to experience an increased risk of dying. As a result of that, we need to continue all of the evidence-based practices we have to try and prevent infection. And, in the event that we are not able to do that, to monitor closely and offer the best evidence-based care that we can to people who become infected,” Hicks stated. She also suggested planning effective strategies for treating patients with blood cancers in the pandemic environment. “Blood cancer itself is a threat to life. This (and other research) offers a little bit of reassurance that we should continue to offer the best evidence-based cancer care that we can in the pandemic environment.” Peter M. Goodwin is a contributing writer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".