Eculizumab's Unintentional Mayhem: A Systematic Review
Bibliographic record
Abstract
Eculizumab, first-line therapy for paroxysmal nocturnal hemoglobinuria (PNH) and atypical hemolytic uremic syndrome (aHUS), has infectious side effects in addition to its therapeutic benefits. This study aims to discuss the mechanism of development of infections, prevention, and timely treatment to prevent complications such as septic shock and mortality. The study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist and reporting guidelines for systematic review. Inclusion and exclusion criteria were determined. A total of 10 research papers were extracted after exploring Pubmed and Google Scholar from 2001 to 2021. The New Castle Ottawa Questionnaire for non-randomized clinical trials and the National Institutes of Health (NIH) quality assessment tool for case reports and case series were used to assess the risk of bias. The studies included in this systematic review describe infections with Neisseria meningitidis, Neisseria gonorrhoeae, unusual Neisseria species, Moraxella lacunata, and Pseudomonas aeruginosa. The main goal of this review is to impress upon the seriousness of the infectious complications associated with eculizumab. Health care providers should maintain a high index of suspicion for early identification and treatment.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".