Significant Transplantation-Related Mortality from Respiratory Virus Infections within the First One Hundred Days in Children after Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Respiratory viral infections (RVI) are important in hematopoietic stem cell transplantations (HSCT) and knowledge regarding incidence, morbidity, mortality, and long-term pulmonary complications is limited. We report a study to evaluate incidence and outcomes, both short and long-term, of RVI in children receiving HSCT. Between January 2000 and December 2012, 844 patients underwent hematopoietic stem cell transplantation (HSCT) at the Hospital for Sick Children: 491 were allogeneic and 353 were autologous. When screening for causes of death in the first year after HSCT in the 844 patients, we found that RVI as a cause of death was only evident in the first 100 days after HSCT. Fifty-four (6.5%) patients were found to have an RVI within the first 100 days after HSCT (allogeneic = 32, autologous = 22). Upper and lower respiratory tract infections were documented in 31 (57%) and 23 (43%) patients, respectively. Viruses were parainfluenza (35%), respiratory syncytial virus (28%), influenza (22%), adenovirus (7%), human metapneumovirus (4%), coronavirus (2%), and rhinovirus (2%). Three patients relapsed with their primary disease before day 100 and were excluded. The overall mortality for the remaining 51 patients was 10% (allogeneic = 4, autologous = 1). All 5 deaths were directly attributable to RVI and all 5 deaths occurred in patients with a lower respiratory tract infection. The remaining patients were followed for a median of 4.3 years (range, 1.4 to 11.8) and no chronic pulmonary complications were observed. A clear seasonal pattern for contracting RVI was evident with 65% of total RVI occurring between October and March (35 of 427 versus 19 of 417, P = .03). Given the significant mortality from RVI and the challenges in preventing them, choosing the time to start HSCT, whenever possible, may help prevent RVI and improve outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".