O022 Characteristics of patients with obstructive sleep apnoea with a focus on the First Nations population
Bibliographic record
Abstract
Abstract Background Obstructive sleep apnoea (OSA) is common with an estimated prevalence of 9-38%. First Nations Australians experience a burden of disease 2.3 times that of the general population. Notably, many of the conditions prevalent in this population are associated with OSA. However, there is a paucity of data relating to the characteristics of First Nations Australians with OSA. Methods This audit aims to analyse the characteristics, co-morbidities, and sleep study data of the cohort of patients referred to our facility for a sleep study between 2010-2023, with a subgroup analysis of the First Nations population. Results To date we have analysed data for 587 patients who attended our facility from 2022-2023 of which 28 (4.7%) identify as First Nations. We analysed 325 diagnostic sleep studies. 315/325 patients were non-First Nations. Of these 61 had a normal study, 88 had mild OSA, 73 had moderate OSA and 93 had severe OSA (as defined by apnoea-hypopnea index of 5-15, 15-30 and >30 respectively). The average BMI was 34.12kg/m2. 10/325 patients identified as First Nations. Of these 3 had a normal sleep study, 3 had mild OSA, 4 had moderate OSA and 2 had severe OSA. The average BMI was 33.98kg/m2. Discussion Data collection and analysis are ongoing. Our small sample size to date likely underestimates the proportion of clients who identify as First Nations. Through this study we hope to gain a deeper understanding of, and contribute to, existing knowledge of specific risk factors present in this group.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".