EXAMINING REFERRAL PATTERNS AND DIAGNOSTIC RATES IN THE BRITISH COLUMBIA AUTISM ASSESSMENT NETWORK
Bibliographic record
Abstract
Abstract BACKGROUND Most children in British Columbia are assessed for ASD by the BC Autism Assessment Network (BCAAN). While all diagnostic clinicians in BCAAN follow the same diagnostic standards, there is no published data on the referral patterns within BCAAN. OBJECTIVES We aimed to examine whether patients referred by different clinician types have different ASD diagnostic rates. DESIGN/METHODS This was a retrospective cross-sectional study using data from the BCAAN database consisting of individual patient data from January 1, 2010 to December 31, 2016. The analysis included all patients aged 0 to 18 years. Sequential logistic regression models were built to look at the effect of referring clinician type on ASD diagnostic outcome. The ASD diagnostic outcome was a binary outcome of whether a child was diagnosed with ASD. The main predictor variables were the referring clinician type. Other predictor variables examined as confounders included age and sex of the child, the referring health region, and the year the child was assessed. Changes in effect size, standard deviation, and Akaike information criterion were used to examine model fit. RESULTS Twenty patients did not have data for referring clinician type, 5 did not have diagnostic outcome data, and only 27 patients were referred by a nurse; they were not included in the analysis. The final dataset included 12,058 unique patients; they were not included in the analysis. The final dataset included 12,058 unique patients; 7271 (60%) were diagnosed with ASD. The ASD positive diagnostic rate for referrals by paediatricians (n=5704) was 61%, by family physicians (n=886) was 60%, by psychiatrists (n=483) was 51%, and by speech language pathologists (SLPs, n=198) was 68%. Using paediatricians as the reference group, the odds ratio of being diagnosed with ASD in the final model for children referred by a family physician was 1 (95% CI = 0.89–1.12); for children referred by a psychiatrist, the odds ratio was 0.87 (95% CI = 0.75–1); for children referred by an SLP, the odds ratio was 1.69 (95% CI = 1.28–2.25). These findings showed that within our study population, children referred by SLPs were more likely to be diagnosed with ASD and there was no statistically significant difference in ASD diagnostic rate for children referred by paediatricians, physicians, and psychiatrists. CONCLUSION Electronic clinical data is a valuable tool in examining referral and diagnostic patterns and generating data guided questions and hypotheses. Findings from this study suggest that the process of SLP referrals as well as differences between family physicians who refer to BCAAN versus the general population of family physicians should be explored as areas for reducing referral delays for children at risk of ASD.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".