Publication trends in obstructive sleep apnea: Evidence of need for more evidence
Bibliographic record
Abstract
Objective Published research in obstructive sleep apnea (OSA) appears limited despite OSA being a highly prevalent adult and pediatric disease leading to many adverse outcomes if left untreated. We aimed to quantify the deficit in OSA scientific literature in order to provide a novel way of identifying gaps in knowledge and a need for further research inquiry. Methods This was a Bibliometric analysis study. Using Ovid Medline database we analyzed and compared research output (medical and surgical) between adult OSA and similarly prevalent chronic conditions (Type II diabetes (T2DM), coronary artery disease (CAD) and osteoarthritis (OA)) from December 2016 up to fifty years prior. Linear graphs were utilized to trend collected data. Utilizing same strategy, we compared publication trends for pediatric OSA to asthma and gastroesophageal reflux (GER). Results Adult OSA publications (n = 9314) were significantly underrepresented when compared to T2DM (n = 66,023), CAD (n = 31,526) and OA (n = 34,123). Linear plots demonstrated that despite increasing number of publications this disparity persisted annually. Surgical literature composed 10.4% (n = 972) of adult OSA publications and reached a plateau in the last ten years. Pediatric OSA (n = 2994) had less research output when compared to asthma (n = 47,442) and GER (n = 6705). However, over past five years pediatric OSA surpassed GER in annual number of publications. Surgical literature represented 23.1% (n = 693) of pediatric OSA publications and continued increasing over past ten years. Study methodologies for both adult and pediatric OSA showed a lack of randomized controlled trials and meta‐analyses in comparison to other diseases. Conclusion Our review shows substantial deficit in total, annual and surgical adult OSA published research compared to similarly prevalent diseases. This trend is not entirely observed in pediatric OSA literature.
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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 | 0.088 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.056 | 0.083 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".