Factors affecting COVID-19 outcomes incancer patients: A first report from Guy's Cancer Centre inLondon
Bibliographic record
Abstract
Background: Current precautionary management decisions being made for cancer patients are based onassumptions supported by limited evidence, based on small case series from China and Italy and larger series fromNew York and a recent consortium of 900 patients from over 85 hospitals in the USA, Canada, and Spain Hence, there is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 dueto the lack of large studies Methods: We used data from a single large UK Cancer Centre to assess demographic/clinical characteristics of 156cancer patients with a confirmed COVID-19 diagnosis between 29 February-12 May 2020 Logistic/Cox proportionalhazards models were used to identify which demographic and/or clinical characteristics were associated withCOVID-19 severity/death Results: 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with severe disease Initial diagnosis ofcancer >24m before COVID-19 (OR:1 74 (95%CI: 0 71-4 26)), presenting with fever (6 21 (1 76-21 99)), dyspnea(2 60 (1 00-6 76)), gastrointestinal symptoms (7 38 (2 71-20 16)), or higher levels of CRP (9 43 (0 73-121 12)) werelinked with greater COVID-19 severity During median follow-up of 47d, 34 patients had died of COVID-19 (22%) Asian ethnicity (3 73 (1 28-10 91), palliative treatment (5 74 (1 15-28 79), initial diagnosis of cancer >24m before(2 14 (1 04-4 44), dyspnea (4 94 (1 99-12 25), and increased CRP levels (10 35 (1 05-52 21)) were positivelyassociated with COVID-19 death An inverse association was observed with increased levels of albumin (0 04 (0 01-0 04) Conclusions: Our analysis of one of the largest single-center series of COVID-19-positive cancer patients to dateconfirms a similar distribution of age, sex, and comorbidities as reported for other populations With respect tocancer-specific observations, patients who have lived longer with their cancer were found to be more susceptible toa greater infection severity, possibly reflecting the effect of more advanced malignant disease, as almost half of thesevere cohort were on third-line metastatic treatment, or the impact of this infection The latter was also found to beassociated with COVID-19 death in cancer patients, as were Asian ethnicity and palliative treatment Furthervalidation will be provided from other large case series, as well as from those including longer follow-up, to providemore definite guidance for oncologic care
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".