Treatment of sudden sensorineural hearing loss: II. A Meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: To pool and meta-analyze the results of all randomized controlled trials (RCTs) on treatment of sudden sensorineural hearing loss (SSHL). DATA SOURCES: A MEDLINE search and hand search were conducted to identify RCTs published between January 1966 and February 2006 in the English language on the treatment of SSHL. Search terms included hearing loss, sensorineural (MeSH term), sensorineural hearing loss (text words), and sudden deafness (text words). STUDY SELECTION: Prospective RCTs on the treatment of patients diagnosed as having sudden sensorineural hearing loss. DATA EXTRACTION: A meta-analysis using the random effects model was conducted when data existed for 2 or more studies. Odds ratios (ORs), 95% confidence intervals (CIs), and tests for heterogeneity were reported. DATA SYNTHESIS: Twenty RCTs were identified, of which 5 met inclusion criteria for meta-analysis. Pooling of data from 2 RCTs that compared steroids with placebo showed no difference between treatment groups (OR, 2.47; 95% CI, 0.89-6.84; P=.08). No difference existed between patients treated with antiviral plus steroid therapy vs placebo plus steroid therapy (OR, 0.92; 95% CI, 0.29-2.92: P=.88). Finally, there was no difference between subjects treated with steroids vs subjects treated with any other active treatment (OR, 1.27; 95% CI, 0.64-2.55; P=.50). CONCLUSIONS: Despite the traditional practice in North America of treating of SSHL with systemic steroids, a meta-analysis revealed no evidence of benefit of steroids over placebo. There was also no difference in the addition of antiviral therapy to systemic steroids, nor was there difference between systemic steroids and other active treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.058 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".