Frequency and impact of paediatric acute-onset neuropsychiatric syndrome/paediatric autoimmune neuropsychiatric disorders associated with streptococcal infections diagnosis in Canada
Bibliographic record
Abstract
Objectives: This study aims to estimate the prevalence of the PANS/PANDAS diagnostic label in Canada and describe its impact on families, patients, and health care. Methods: Through the Canadian Paediatric Surveillance Program (CPSP), a monthly form was distributed to paediatricians from December 2019 to November 2021, requesting reports of children who received the diagnostic label of PANS/PANDAS between the ages of 3 and 18 years seen in the previous month. Descriptive and association statistical analyses were performed. Results: Eighty-four cases (57% female, median age of symptom onset 7.8 years interquartile range [IQR] = 5) who received the diagnostic label of PANS/PANDAS were included. Prevalence was found to be 1 in 60,155 (0.0017%). Core diagnostic criteria for PANS/PANDAS (obsessive-compulsive disorder or tics or acute food refusal) were not present in 12% of cases (10/84). Only 22% reported sudden symptom onset. Infection was associated with symptom onset or exacerbation in less than one-third of cases. The majority exhibited two or more neuropsychiatric symptoms (95%). There was significant health care utilization and symptom burden amongst cases. There was a significant difference in the certainty of diagnosis between physicians and families (P < 0.05). Conclusions: PANS/PANDAS diagnoses, while rare, significantly impact children, families, and the health care system. Diagnostic uncertainty underscores the challenges professionals and families face in accessing effective care, emphasizing the need for education and evidence-based clinical practice guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".