Anti‐dopamine D2 receptor antibodies in chronic tic disorders
Bibliographic record
Abstract
AIM: To investigate the association between circulating anti-dopamine D2 receptor (D2R) autoantibodies and the exacerbation of tics in children with chronic tic disorders (CTDs). METHOD: One hundred and thirty-seven children with CTDs (108 males, 29 females; mean age [SD] 10y 0mo [2y 7mo], range 4-16y) were recruited over 18 months. Patients were assessed at baseline, at tic exacerbation, and at 2 months after exacerbation. Serum anti-D2R antibodies were evaluated using a cell-based assay and blinded immunofluorescence microscopy scoring was performed by two raters. The association between visit type and presence of anti-D2R antibodies was measured with McNemar's test and repeated-measure logistic regression models, adjusting for potential demographic and clinical confounders. RESULTS: At exacerbation, 11 (8%) participants became anti-D2R-positive ('early peri-exacerbation seroconverters'), and nine (6.6%) became anti-D2R-positive at post-exacerbation ('late peri-exacerbation seroconverters'). The anti-D2R antibodies were significantly associated with exacerbations when compared to baseline (McNemar's odds ratio=11, p=0.003) and conditional logistic regression confirmed this association (Z=3.49, p<0.001) after adjustment for demographic and clinical data and use of psychotropic drugs. INTERPRETATION: There is a potential association between immune mechanisms and the severity course of tics in adolescents with CTDs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".