Otolaryngology needs among an adult homeless population: A prospective study
Bibliographic record
Abstract
BACKGROUND: Homeless individuals frequently experience poor access to healthcare, delayed clinical presentation, and higher disease burden. Providing subspecialty otolaryngology care to this population can be challenging. We previously reported on the prevalence of hearing impairment in Toronto's homeless community. As a secondary objective of this study, we sought to define otolaryngology specific need for this population. METHODS: One hundred adult homeless individuals were recruited across ten homeless shelters in Toronto, Canada using a stratified random sampling technique. An audiometric evaluation and head and neck physical examination were performed by an audiologist and otolaryngology resident, respectively. Basic demographic and clinical information was captured through verbal administration of a survey. Descriptive statistics were used to estimate frequency of otolaryngology specific diseases for this population. RESULTS: Of the 132 individuals who were initially approached to participant, 100 (76%) agreed. There were 64 males, with median age of 46 years (IQR 37-58 years). The median life duration of homelessness was 24 months (IQR 6-72 months). Participants had a wide range of medical comorbidities, with the most common being current tobacco smoking (67%), depression (36%), alcohol abuse (32%), and other substance abuse (32%). There were 22 patients with otolaryngology needs as demonstrated by one or more abnormal findings on head and neck examination. The most common finding was nasal fracture with significant nasal obstruction (6%). Eleven patients required referral to a staff otolaryngologist based on concerning or suspicious findings, including two head and neck masses, 6 were later seen in follow-up. CONCLUSION: There were substantial otolaryngology needs amongst a homeless population within a universal healthcare system. Future research should focus on further elucidating head and neck related issues in this population and expanding the role of the otolaryngologist in providing care to homeless individuals.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".