Evaluating the referral preferences and consultation requests of primary care physicians with otolaryngology – head and neck surgery
Bibliographic record
Abstract
BACKGROUND: No literature exists which examines referral preferences to, or the consultation process with, Otolaryngology. In a recent Canadian Medical Association nation-wide survey of General Practitioners and Family Physicians, Otolaryngology was listed as the second-most problematic specialty for referrals. The purpose of this study was to learn about and improve upon the referral process between primary care physicians (PCPs) and Otolaryngology at an academic centre in Southwestern Ontario. METHODS: PCPs who actively refer patients to Otolaryngology within the catchment area of Western University were asked to complete a short paper-based questionnaire. Data was analyzed using descriptive statistics. RESULTS: A total of 50 PCPs were surveyed. Subspecialty influenced 90.0% of the referrals made. Specialist wait times altered 58.0% of referrals. All PCPs preferred to communicate via fax. Half of those surveyed wanted clinical notes from every encounter. Seventy-four percent of respondents wanted inappropriate referrals forwarded to the proper specialist automatically. Twenty-two percent of those surveyed were satisfied with current wait times. A central referral system was favored by 74% of PCPs. CONCLUSION: Improvements could help streamline the referral and consultation practices with Otolaryngology in Southwestern Ontario. A central referral system and reduction in the frequency of consultative reports can be considered.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".