No Cases of PANDAS on Follow-Up of Patients Referred to a Pediatric Movement Disorders Clinic
Bibliographic record
Abstract
INTRODUCTION: Pediatric autoimmune neuropsychiatric disorders associated with streptococcal infection (PANDAS) remains a controversial diagnosis and it is unclear how frequently it is encountered in clinical practice. Our study aimed to determine how many children with acute-onset tics and/or Obsessive-Compulsive Disorder (OCD) met criteria for PANDAS. MATERIALS AND METHODS: A retrospective review was performed on 39 children who presented to a movement disorders clinic with acute-onset tics or OCD from 2005 to 2012. RESULTS: Out of 284 patients seen over the course of 7 years, only 39 had acute-onset tics and/or OCD symptoms. None of the 39 children who presented to us acutely met full criteria for PANDAS. Thirty-eight percent had no association between their symptoms and group A beta-hemolytic streptococcal infection, while 54% had prior inconclusive laboratory testing done and no exacerbations during the course of the study. Only 8% of patients had an acute exacerbation after their initial visit; however, testing for GAHBS in these patients was negative Discussion: Our results support the notion that PANDAS, if it exists, is an exceedingly rare diagnosis encountered in a pediatric movement disorder clinic. While none of our patients met criteria for PANDAS, two with acute-onset OCD would have met criteria for pediatric acute-onset neuropsychiatric syndrome (PANS) indicating that PANS may be a more appropriate diagnosis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".