Assessment of multiple-opinion referrals and consults at the BC Children’s Hospital Allergy Clinic
Bibliographic record
Abstract
BACKGROUND: Allergic disease is on the rise. Waitlists for specialists are long, and many referred patients have already received prior allergic assessment, either by a certified Allergist, Primary Care Provider, or other Specialist. It is important to understand the prevalence and motivating factors for multiple-opinion referrals, to deliver timely assessment for patients with allergic disease. METHODS: A retrospective chart review of demographic information, number of previous consultations, and motivation for new consults and multiple-opinion referrals, of pediatric patients aged 8 months-17 years to BC Children's Hospital Allergy Clinic from September 1, 2016-August 31, 2017, was performed. Referral data including reason for referral or multiple-opinion, primary allergic concerns, and others, from referral forms and consult notes were accessed through local Electronic Medical Records and subsequently analyzed for trends in categorical variables to assess the rationale for and impact of multiple-opinion referrals to our clinic. RESULTS: Of 1029 new referrals received, 210 (20.4%) were multiple-opinion referrals. Food allergy was the predominant allergic concern prompting further opinion (75.7%). The main rationale for seeking further opinions was wanting an assessment by a certified allergist in cases where prior consultation was performed by non-allergist specialist, primary care provider, or alternative health care provider. Of second-opinion referrals generated, 70 (33.3%) initial consultations were performed by an Allergist, whereas 140 (66.7%) were performed by a non-allergist. CONCLUSIONS: Many new consults at the BCCH Allergy Clinic are multiple-opinion assessments, contributing to long waitlists. Advocacy at the systems level through standardized referral guidelines, centralized triaging systems, and stronger support for Primary Care Providers is needed to provide better access in Canada for children needing a specialized Allergist. Trial registration UBC/BCCH Research Ethics Board.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".