89 Frequency and Impact of PANDAS/PANS Diagnosis in Canada
Bibliographic record
Abstract
Abstract Background Paediatric autoimmune neuropsychiatric disorders associated with streptococcal infection (PANDAS/PANS) are defined by abrupt onset and episodic course of neuropsychiatric symptoms associated with infection. There are challenges in the application of published diagnostic criteria, and the frequency of diagnosis is unknown. To date, there is little data on clinical practice patterns in diagnosis, assessment and treatment. Objectives The aim of this study was to estimate and characterize the frequency and impact of the diagnostic label of PANDAS/PANS in Canada. Design/Methods Through established CPSP methodology, over 2,800 paediatricians and subspecialists were surveyed for children seen in the previous month who had received the diagnostic label of PANDAS/PANS. Respondents completed a detailed questionnaire on demographics, clinical presentation, diagnostic evaluation and interventions. Results Between December 1, 2019 and November 30, 2021, 84 reported cases received the diagnosis of PANDAS/PANS. The majority were female (48/84, 57%), with mean age of symptom onset 9.4 years (range 3 to 16 years). Of the 84 cases, 82% (69/84) had OCD and/or acute food refusal, 39% (33/84) had tics and 32% (27/84) had both OCD and tics. Symptom onset was described as sudden in 22% of cases. The vast majority of cases had at least two neuropsychiatric symptoms (80/84, 95%). Infection was associated with symptom onset (22/84, 26%) or exacerbation (25/84, 30%) in less than one-third of cases. Healthcare utilization included emergency-department visits (29/84, 35%) and inpatient admissions, both medical (8/84, 10%) and psychiatric (8/84, 10%). Three quarters (52/68, 76%) of cases had five or more health care visits since symptom onset and 31% (24/78, 31%) accessed more than five health care providers. Medical treatments were provided in 95% (80/84; antibiotics, NSAIDS, steroids, IVIG or rituximab), 95% (80/84) received psychological and/or psychiatric treatments, and more than 50% (42/84) of patients saw a complementary/alternative health provider. Almost all cases (84/85) experienced significant negative impacts, including family stress, mental health concerns, or conflict (54/84, 64%); school absences (43/84, 51%); and withdrawal from activities/friends (33/84, 39%). In forty-one percent (33/81) of cases, there was a significant discrepancy in the certainty of diagnosis, with families significantly more certain than reporting physicians. Conclusion PANDAS/PANS are rare diagnoses, associated with a significant burden on children, families and the healthcare system. In reported cases, there was significant practice variation in diagnosis, assessment and treatment, with frequent uncertainty about the diagnosis. Education and clinical practice guidelines are needed, targeting a range of practitioners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".