Consultation diagnoses and procedures billed among recent graduates practicing general otolaryngology – head & neck surgery in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: An analysis of the scope of practice of recent Otolaryngology - Head and Neck Surgery (OHNS) graduates working as general otolaryngologists has not been previously performed. As Canadian OHNS residency programs implement competency-based training strategies, this data may be used to align residency curricula with the clinical and surgical practice of recent graduates. METHODS: Ontario billing data were used to identify the most common diagnostic and procedure codes used by general otolaryngologists issued a billing number between 2006 and 2012. The codes were categorized by OHNS subspecialty. Practitioners with a narrow range of procedure codes or a high rate of complex procedure codes, were deemed subspecialists and therefore excluded. RESULTS: There were 108 recent graduates in a general practice identified. The most common diagnostic codes assigned to consultation billings were categorized as 'otology' (42%), 'general otolaryngology' (35%), 'rhinology' (17%) and 'head and neck' (4%). The most common procedure codes were categorized as 'general otolaryngology' (45%), 'otology' (23%), 'head and neck' (13%) and 'rhinology' (9%). The top 5 procedures were nasolaryngoscopy, ear microdebridement, myringotomy with insertion of ventilation tube, tonsillectomy, and turbinate reduction. Although otology encompassed a large proportion of procedures billed, tympanoplasty and mastoidectomy were surprisingly uncommon. CONCLUSION: This is the first study to analyze the nature of the clinical and surgical cases managed by recent OHNS graduates. The findings demonstrated a prominent representation of 'otology', 'general' and 'rhinology' based consultation diagnoses and procedures. The data derived from the study needs to be considered as residency curricula are modified to satisfy competency-based requirements.
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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 | 0.000 | 0.003 |
| 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) | 0.003 | 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".