Haemopoietic stem cell transplantation in Systemic lupus erythematosus: a systematic review
Bibliographic record
Abstract
Despite advances in treating Systemic lupus erythematosus (SLE), a proportion of patients continue to face significant morbidity and mortality. Haemopoietic stem cell transplant (HSCT) has been recognized as an option for such patients. We analysed the evidence on efficacy and safety of HSCT in patients with SLE. A database search was done for articles on HSCT in SLE up to July 2017 in PUBMED, Cochrane library, LILACS and clinical trial registration databases to select prospective or retrospective studies with 8 or more patients. Of the 732 search results from the PUBMED, Cochrane and LILACS database search, following duplicate removal, 15 studies were eligible for detailed assessment. Findings of an additional trial were obtained from the clinical trial registration database. Data were extracted on study design, patient characteristics, nature of intervention, outcomes, complications and study quality. Case reports and small case series were summarised without detailed qualitative analysis. Most of the studies showed remission in the majority of patients. Relapse of the original disease increased with longer follow-up. Common adverse effects included: infections and secondary autoimmune disorders. Short follow up period and lack of randomised controlled trials were the main limitations restricting the generalizability of study results. A meta-analysis was not performed due to heterogeneity of studies. Although HSCT is a viable option in SLE, its exact clinical utility needs to be further evaluated in well-designed studies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".