Sudden Sensorineural Hearing Loss and Metabolic Syndrome: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The objective of this systematic review and meta-analysis is to examine the association between sudden sensorineural hearing loss (SSNHL) and risk of metabolic syndrome (MetS), and the association between MetS and prognosis of SSNHL. DATABASES REVIEWED: We systematically searched MEDLINE, Embase, and Cochrane Central Register electronic databases from their dates of conception to February 4, 2020. METHODS: We included observational studies analyzing 1) the prevalence of MetS among SSNHL patients, or 2) the prognosis of SSNHL patients in MetS patients. A standardized form was completed in duplicate extracting data on study characteristics, participant demographics, and SSNHL outcome or recovery measures. Random-effects meta-analyses were performed pooling odds ratios using the generic inverse method. Risk of bias was assessed using the Newcastle Ottawa Scale. RESULTS: Three studies examining the prevalence of MetS among patients with SSNHL (11,890 total participants; 3,034 SSNHL participants) yielded a significantly increased risk of MetS among SSNHL, with a pooled odds ratio of 1.88 (95% CI, 1.01-3.50). Three studies examining the association of SSNHL prognosis in patients with MetS (608 SSNHL participants, 234 concomitant SSNHL, and MetS participants) demonstrated that SSNHL patients with MetS were significantly more likely to have poorer recovery compared to SSNHL patients without MetS (pooled odds ratio 2.77; 95% CI, 2.33-3.28). CONCLUSION: Our findings suggest an association between prevalence of MetS and SSNHL, as well as poorer prognosis of SSNHL in patients with concomitant MetS.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".