A Portrait of SARS-CoV-2 Infection in Patients Undergoing Hematopoietic Cell Transplantation: A Systematic Review of the Literature
Bibliographic record
Abstract
The management of COVID-19 in hematopoietic cell transplant (HCT) recipients represents a special challenge given the variable states of immune dysregulation and altered vaccine efficacy in this population. A systematic search (Ovid Medline and Embase on 1 June 2021) was needed to better understand the presenting features, prognostic factors, and treatment options. Of 897 records, 29 studies were identified in our search. Most studies reporting on adults and pediatric recipients described signs and symptoms that were typical of COVID-19. Overall, the mortality rates were high, with 21% of adults and 6% of pediatric HCT recipients succumbing to COVID-19. The factors reported to be associated with increased mortality included age (HR = 1.21, 95% CI 1.03–1.43, p = 0.02), ICU admission (HR = 4.42, 95% CI 2.25–8.65, p < 0.001 and HR = 2.26, 95% CI 1.22–4.20, p = 0.01 for allogeneic and autologous HCT recipients), and low platelet count (OR = 21.37, 95% CI 1.71–267.11, p = 0.01). Performance status was associated with decreased mortality (HR = 0.83, 95% CI 0.74–0.93, p = 0.001). A broad range of treatments was described, although no controlled studies were identified. The risk of bias, using the Newcastle–Ottawa scale, was low. Patients undergoing HCT are at a high risk of severe morbidity and mortality associated with COVID-19. Controlled studies investigating potential treatments are required to determine the efficacy and safety in this population.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".