Assessment of Multiple-Opinion Referrals and Consults at the BC Children’s Hospital Allergy Clinic
Bibliographic record
Abstract
Abstract Background: Allergic disease is on the rise. Waitlists for specialists are long, and many referrals have already received prior allergic assessment. It is important to understand the prevalence and motivating factors for second opinion referrals, to deliver timely assessment for patients with allergic disease. Methods:A retrospective chart review of demographic information, referral patterns, and motivation for new consults of pediatric patients aged 8 months – 17 years to BC Children’s Hospital Allergy Clinic from September 1, 2016 – August 31, 2017, was performed. Data were accessed through local Electronic Medical Records and subsequently analyzed for frequency and motivation for 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 another opinion (75.7%). The main rationale for seeking further opinions was looking to speak with a certified Allergist, or dissatisfaction with previous opinions. Conclusions: Many new consults at the BCCH Allergy Clinic are multiple-opinion assessments, contributing to long waitlists and poor patient satisfaction. Advocacy at the systems level is needed to provide better access in Canada for children needing a specialized Allergist. Trial Registration - UBC/BCCH Research Ethics Board (H18-02528)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".