Relationship between Nasal Septum Deviation and Size of Inferior Turbinate in Obstructive Sleep Apnea: A Systematic Review
Bibliographic record
Abstract
Abstract Background: Obstructive sleep apnea is increasing in prevalence, with multiple risk factors, but the relationship between nasal septum deviations and inferior turbinate size remains controversial. Objective: This systematic review examines the link between obstructive sleep apnea (OSA) and upper airway abnormalities, specifically nasal septum deviation and inferior turbinate hypertrophy. Methods: Registered in PROSPERO [CRD42024523975], the review used a two-phase search in six databases, including eligible studies while excluding small samples, animal studies, case reports, reviews, and non-full-text articles. Two reviewers extracted data, with findings summarized narratively and analyzed using risk ratios and standardized mean differences. Study quality was assessed via the Newcastle-Ottawa Scale. Results: This review of nine studies [five case-control, four cohort] found a strong association between nasal septum deviation (NSD), inferior turbinate hypertrophy (ITH), and OSA in 34,742 participants, with low bias risk and reliable diagnostic methods. Conclusion: The systematic review suggests assessing obstructive sleep apnea patients for nasal septum deviations and inferior turbinate hypertrophy, and recommending specialized training for healthcare and dental professionals for improved diagnosis and treatment outcomes. Further research is needed.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".