Reducing Aminoglycoside Ototoxicity
Bibliographic record
Abstract
Objective: The objective of this project was to determine whether sodium thiosulphate (STS) could be used clinically to reduce hearing loss caused by gentamicin, an aminoglycoside. First, we established the appropriate dose of gentamicin to induce a mild to moderate sensorineural hearing loss and the most appropriate time to test the hearing loss. Second, we used this model to assess the protective effects of STS and α-tocopherol on gentamicin induced ototoxicity. Methods: C57 male mice (4 – 5 weeks old, 150 – 200g). Phase 1: Thirty mice received gentamicin in increasing doses from 160-220 mg/kg and underwent auditory brainstem response (ABR) testing to assess hearing changes over 3 months. This dose-response relationship was used to optimize Phase2. Phase 2: Six groups of 64 mice were studied. Group1: gentamicin 220 mg/kg i.p. with isotonic (25%) STS 1600 mg/kg. Group2: gentamicin 220 mg/kg i.p. with α-tocopherol 400 mg/kg. Group3: STS 1600 mg/kg i.p. Group4: α-tocopherol 400 mg/kg i.p. Group5: saline i.p. Group 6: gentamicin 220 mg/kg i.p. ABRs were recorded at baseline and at 30 days. Results: Phase 1: The optimal mouse model for this type of research should employ a dose of 220 mg/kg and study animals at one month. Phase 2: Threshold changes for the treatment group that received STS with gentamicin were not statistically significantly different from gentamicin without STS. Gentamicin induced threshold shifts of about 15 dB at one month and great variability was found in the response. Conclusion: STS does not offer protection against gentamicin induced hearing loss.
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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 | 0.000 | 0.000 |
| 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".