Ovarian resection in anti-N-methyl-D-aspartate receptor encephalitis: A comparison of surgical approaches
Bibliographic record
Abstract
Background For patients with anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) and ovarian teratoma, “conservative” surgical approaches (complete or partial unilateral oophorectomy or bilateral partial oophorectomies) are associated with clinical improvement. “Aggressive” ovarian resections (complete bilateral oophorectomy or “blind” ovarian resections without pre-operative evidence of teratoma) are also reported, although the evidence supporting these approaches is unclear. Objective To compare the one-year functional outcomes of patients with NMDARE who underwent conservative vs. aggressive ovarian resections. Methods Patients with NMDARE undergoing ovarian resection between January 1st, 2012 and December 31st, 2021 were retrospectively identified from three North American tertiary care centers. Primary outcome was a modified Rankin Scale score of 0–2 one year after ovarian resection. Fisher exact and Wilcoxon rank sum tests were used to compare demographic features, disease characteristics, and functional outcomes between the two surgical groups. A fixed-effects meta-analysis of studies reporting functional outcomes based on surgical approach was also performed. Results Twenty-three patients were included. Eight underwent aggressive surgical management. There was a non-significant trend toward an association between aggressive surgical management and younger age-at-onset, higher baseline disease severity, and longer delays to treatment. There was no difference between “aggressive” (3/8, 38%) and “conservative” (11/15, 73%) management groups in achieving the primary outcome (OR95% = <0.1–1.9; p = 0.18). Findings were similar when considering data from 52 patients in two published studies (RR = 0.74; CI95% = 0.48–1.13; p = 0.16). Conclusions Aggressive ovarian resection was not associated with improved outcomes in patients with NMDARE in this series. Group differences may have contributed, recognizing that patients who underwent aggressive resection tended to be sicker, with procedures performed later in the disease course. Based on available evidence, we advocate for function-sparing resection in patients with imaging-confirmed/suspected teratoma, and repeated multi-modal imaging in at-risk patients with NMDARE refractory to conventional treatment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".