46 Patterns of referrals for evaluation of Autism Spectrum Disorder in the pediatric population of Saskatchewan as a predictor of diagnostic outcome
Bibliographic record
Abstract
Abstract Background The Alvin Buckwold Child Development Program (ABCDP) serves Northern and Central Saskatchewan as a tertiary diagnostic centre for Autism Spectrum Disorder (ASD) but data is lacking on referrals and diagnostic outcome. Objectives This study aimed to assess if referral patterns are associated with diagnostic outcome of ASD. Design/Methods We conducted a retrospective cross-sectional study of patients from the ABCDP who were referred and assessed for ASD, or who received a diagnosis of ASD, from January 1st, 2016 to December 31st, 2018. Predictor variables were the referring clinician type and referral content. Suspicion for ASD from referrals were reported as being concerning for one, both or none of the DSM-5 core criteria for ASD (social communication and interaction “criteria A”; restricted and repetitive behaviors “criteria B”). When available, speech-language pathologist (SLP) reports were reviewed to identify concerns of social communication. The main outcome was a diagnosis or not of ASD. Risk ratio (RR) and odds ratio (OR) were estimated by simple logistic regression models to assess the association between individual variables of referral patterns and diagnosis of ASD. Results The final dataset included 527 patients after excluding 128 patients (123 were not seen at our center and 5 had a virtual assessment of ASD). A total of 365 patients received a diagnosis of ASD, 140 did not receive a diagnosis, 21 had their diagnosis deferred and one had this information missing. Referrals by physiotherapist (PT) were 46% more likely than other sources of referrals to lead to a diagnosis of ASD (RR=1,46; 95% CI 1,37-1,54; p=0,022). Referrals by SLP were 25% more likely than other sources of referrals to lead to a diagnosis of ASD (RR=1,25; 95% CI 1,12-1,39; p<0,0001). Referrals identifying concerns of social communication in SLP reports were 29% more likely than other sources of referrals to lead to a diagnosis of ASD (RR=1,29; 95% CI 1,16-1,43; p<0,0001). Using no criteria as reference group, referrals with concerns of criteria A were about 2.7 times more likely to lead to a diagnosis of ASD (OR=2,69; 95% CI 1,427-5,054; p=0,002). Conclusion In our study population, we found that certain referring clinician type (SLP, PT) and content of referrals (concerns for social communication in SLP reports, main concerns related to criteria A) are more likely to lead to a diagnosis of ASD. These findings could be used to identify referrals with a higher risk of ASD and allocate resources accordingly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".