A visiting otolaryngology team in northern Ontario - demographics, clinical presentation and barriers to access
Bibliographic record
Abstract
CONTEXT: Approximately 20% of Canadians reside in rural or remote communities where access to medical specialties such as otolaryngology remains challenging due to long wait times and distance to services. The purpose of this study was to characterize patient demographics, common clinical diagnoses, and barriers to accessing otolaryngology services, in a remote Northern Ontario setting. A secondary objective was to describe a care model that provides multi-subspecialty otolaryngology services to a remote community. ISSUE: A team of academic otolaryngologists provided annual (2020-2021) subspecialty services in otology, neurotology, rhinology, head and neck oncology, and pediatrics to a remote hospital with admitting, general anesthesia and surgical resources. Data regarding patient demographics, otolaryngology-related diagnosis, wait times and distance travelled were recorded. Data were obtained for 276 patients treated in the clinic. The median age was 47 years (range 0-85 years). The most common otolaryngological conditions were hearing loss (n=62) and nasal obstruction (n=34). Nearly 30% of patients traveled further than 150 km to access care, and 62% waited 3-6 months for a consultation. LESSONS LEARNED: This is the first study to characterize the demographics and range of otolaryngological disorders encountered in a remote Northern Ontario setting. The results have identified specific otolaryngology needs and barriers to access to care. The data can be used to guide healthcare providers and administrators on resource allocation to optimize the delivery of otolaryngology services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.004 | 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".