Efficacy and safety of blood purification in the treatment of autoimmune encephalitis: a meta-analysis
Bibliographic record
Abstract
Objective: To systematically evaluate the efficacy and safety of blood purification in the treatment of autoimmune encephalitis (AE).Methods: Databases including PubMed, Embase, and Cochrane Library were systematically searched.Prospective and retrospective cohort studies were included.Data on patients' baseline characteristics, interventions, and outcomes were extracted.The Newcastle-Ottawa Scale (NOS) was used to assess the quality of included studies.Meta-analysis was performed using RevMan 5.4 software.Results: Fifteen studies (531 patients) were included; NOS scores of 7-9 indicated high quality.Efficacy analysis showed that in studies with control groups, blood purification significantly increased the likelihood of clinical improvement (Odds Ratio (OR)=5.61,95% Confidence Interval (CI) [2.72, 11.56], P<0.00001).In studies without control groups, most efficacy indicators (e.g., clinical improvement, modified Rankin Scale (mRS) score improvement) showed statistical significance.Safety analysis revealed that the risk of therapeutic plasma exchange (TPE)-related adverse events was significantly increased (Risk Difference (RD)=0.46,95% CI [0.40, 0.52], P<0.00001).The risks of complications and seizures were also elevated (RD=0.57and 0.74, respectively, both P<0.05).The risk of total adverse reactions per cycle was increased (RD=0.09,95% CI [0.04, 0.14], P=0.0004).The 1-year relapse risk was significantly increased (RD=0.07,95% CI [0.02, 0.11], P=0.004), while there was no significant difference in mortality (P>0.05).Publication bias was assessed via funnel plots and Egger's test, with no evidence of bias, and sensitivity analysis results were stable.Conclusion: Blood purification can significantly improve clinical outcomes in AE patients, but it is associated with higher risks of adverse events and relapse.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".