Can we use the oxygen desaturation index alone to reliably diagnose obstructive sleep apnoea in obese patients?
Bibliographic record
Abstract
Background: Sleep studies at St.Peter9s Hospital are performed upon request of the respiratory and bariatric health departments. There are a finite number of sleep study kits, that can fail to meet the demand, and results in delays in diagnosis and treatment. Subjectively, in those with high body mass indexes (BMI), the oxygen desaturation index (ODI) was thought to be closely related to the apnoea-hyponeic index (AHI), suggesting that a diagnosis of sleep apnoea in these patients could be made with oxygen saturation monitoring alone. Objective: To qualify the link between ODI and AHI in patients suspected of having sleep apnoea. Methods: A retrospective analysis of 6 months worth of sleep study data at St. Peter9s Hospital. We focused on all studies performed on patients with a BMI of over 40. We focused on the AHI, ODI, Weight/ BMI and age of the patients involved. Results: There were 79 patients with a BMI of over 40 who had a sleep study performed. 74 of these studies were positive for sleep apnoea, and we found a strong correlation between the ODI and AHI (Spearmans Rank correlation = 0.976). Conclusion: Given the strong correlation between ODI and AHI demonstrated, we would consider monitoring oxygen levels alone overnight for the diagnosis of sleep apnoea in people with a BMI of over 40. This would enable us to perform more studies and aim to start people on treatment sooner.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".