Global Risk Factor Evaluation of Obstructive Sleep Apnea in Relation to Research Activity and Socioeconomic Factors
Bibliographic record
Abstract
Obstructive Sleep Apnea is emerging as a global health epidemic, particularly due to the obesity pandemic. However, comprehensive prevalence data are still lacking and global OSA research has not yet been structurally evaluated. Using the latest comprehensive age/gender-specific BMI and obesity data, a global landscape estimating the risk/burden of OSA was created. Results were presented in relation to an in-depth analysis of OSA research and countries' socioeconomic/scientific background. While the USA, Canada, and Japan are the highest publishing countries on OSA, Iceland, Greece, and Israel appeared at the forefront when relating the scientific output to socioeconomic parameters. Conversely, China, India, and Russia showed relatively low performances in these relations. Analysis of the estimated population at risk (EPR) of OSA showed the USA, China, India, and Brazil as the leading countries. Although the EPR and OSA research correlated strongly, major regional discrepancies between the estimated demand and actual research performances were identified, mainly in, but not limited to, developing nations. Our study highlights regional challenges/imbalances in the global activity on OSA and allows targeted measures to mitigate the burden of undiagnosed/untreated OSA. Furthermore, the inclusion of disadvantaged countries in international collaborations could stimulate local research efforts and provide valuable insights into the regional epidemiology of OSA.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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".