Noise-Induced Hearing Loss Treatment: Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy of steroid and hyperbaric oxygen therapy (HBOT) in the setting of acute noise-induced hearing loss. METHODS: Systematic review and meta-analysis of noise-induced hearing loss treatment studies that reported on patients who (1) reported individual frequencies up to 8,000 Hz with mean and SDs; (2) were treated only with steroids ± HBOT; and (3) sustained acute acoustic trauma. The Newcastle-Ottawa Scale was used to assess risk of bias across cohorts. Data sources were Embase, Web of Science, Cochrane Databases (via Ovid EBM Reviews), and PubMed. RESULTS: Four studies were of retrospective cohorts and one of a prospective cohort. Only one study examined blast acoustic trauma, and the remaining four examined gunfire acoustic trauma. This meta-analysis used a random-effects model for pure tone average (PTA) (0.5, 1, and 2 kHz) and "high-frequency" PTA (HPTA) (4, 6, and 8 kHz) for the five studies included. Steroid therapy demonstrated a 6.55-dB (95% CI, 0.08-13.17 dB) PTA (n = 55) improvement and a 9.02-dB (95% CI, 1.45-16.59 dB) HPTA (n = 71) improvement. Steroid with HBOT demonstrated a 7.00-dB (95% CI, 0.84-13.17 dB) PTA (n = 133) improvement and a 12.41-dB (95% CI, 3.97-20.86 dB) HPTA (n = 150) improvement. According to our statistical analysis of the pooled studies' heterogeneity, there was moderate inconsistency in the cross-study results of both treatment groups. CONCLUSION: Steroids with or without HBOT appear to improve both low and high hearing thresholds following acoustic trauma. Future studies will require inclusion of control groups, precise definition of acoustic trauma intensity and duration, and genetic polymorphisms.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.005 | 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) | 0.005 | 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".