Burden of illness of non-hematopoietic stem cell transplant-related hepatic sinusoidal obstruction syndrome: A systematic review
Bibliographic record
Abstract
Background: Sinusoidal obstruction syndrome (SOS)/veno-occlusive disease (VOD) is generally associated with hematopoietic cell transplant (HCT), but little is known about this condition outside the HCT setting. This systematic review examines the burden of illness and current management approaches in non-HCT SOS/VOD. Methods: We searched Embase, Medline, and grey literature sources for non-HCT SOS/VOD studies published 2002-2023. Inclusion criteria were studies of any design reporting incidence, diagnosis, underlying disease and any ongoing treatment at the time of SOS/VOD onset, management of non-HCT SOS/VOD, clinical burden, health-related quality of life, healthcare resource use, costs, and patients' unmet needs. Studies investigating pulmonary VOD or SOS/VOD related to the ingestion of pyrrolizidine alkaloids were excluded.Two authors independently screened results, extracted data, and assessed the methodological quality of studies using the Motheral scale for retrospective studies, Newcastle-Ottawa scale for prospective studies and case control studies, the Cochrane risk of bias tool for randomized controlled trials, and the Joanna Briggs Institute critical appraisal tool for case series. Results were synthesized narratively. Results: Ninety-two studies were included; 57 % were retrospective cohort studies and 70 % were conducted in the US or Europe. The study populations included hematological and solid tumor cancers, various indications for liver transplant, Wilms' tumor, and transfusion-dependent beta thalassemia. Non-HCT SOS/VOD occurs most frequently in people with colorectal liver metastases (CRLM), acute lymphoblastic leukemia (ALL), and acute myeloid leukemia (AML). Approximately 35 % of oxaliplatin-treated CRLM patients and 5 % of ALL and AML patients have non-HCT SOS/VOD. Diagnosis varies according to initial disease setting. Defibrotide is the most frequently reported treatment. Most studies did not clearly report their data sources or methods of outcome assessment. Conclusion: Non-HCT SOS/VOD occurs in diverse disease conditions, therefore guidelines on diagnosis and treatment are needed to optimize management in clinical practice.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".