Adjunctive Hyperbaric Oxygen Therapy or Intratympanic Steroids in Sudden Sensorineural Hearing Loss?
Bibliographic record
Abstract
OBJECTIVES: The pathophysiology of sudden sensorineural hearing loss (SSNHL) is still unknown, and therefore treatment strategies are often debated. Traditionally, SSNHL is treated with steroids, either orally (OCS) and/or via intratympanic injection (ITSI). Hyperbaric oxygen treatment (HBOT) has resurged in popularity as an adjunctive therapy. The present study investigated the additive effect of HBOT to traditional steroid treatment for SSNHL. METHODS: Retrospective study comparing treatment effect (pure tone average-PTA; speech recognition threshold-SRT; word recognition score-WRS) between HBOT + ITSI and ITSI treated patients. Sub-analysis of responders and nonresponders, treatment delay, and number of injection/dives. RESULTS: One hundred nineteen patients were divided into ITSI (n = 73) and HBOT + ITSI (n = 46) groups. While there was a significant pre-to-posttreatment improvement in PTA, SRT, and WRS (p < 0.001) within each group, there was no difference between groups in pre-to-postimprovement for PTA, SRT, or WRS (p = 0.49, 0.07, or 0.55, respectively). Of responders to treatment, 4.1% did not receive OCS compared to 24.4% of nonresponders (p < 0.001). In HBOT responders, audiogram improvement was demonstrated within 10.9 ± 6.5 (max 23) sessions. 25.8% of HBOT responders showed no response after completing ITSI and then subsequently demonstrated audiometric response after 17.5 ± 4.0 HBOT dives. CONCLUSION: No additional treatment benefit was found with adjunctive concurrent HBOT. HBOT might be of value to patients refractory to steroid treatment. No beneficial treatment effect in receiving more than 23 HBOT dives was observed. However, evaluating treatment effect in SSNHL loss is always biased by the well-known confounders that are linked to the condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".