Prevention of aminoglycoside-induced sensorineural hearing loss.
Bibliographic record
Abstract
BACKGROUND: Aminoglycoside antibiotics are some of the most commonly used agents for treating gram-negative bacterial infections. They are extremely efficacious but can result in ototoxicity. It has been postulated that the mechanism inducing damage is the formation of oxygen free radicals. Many compounds have been employed in an attempt to reduce aminoglycoside-induced hearing loss. We endeavour to do likewise using sodium thiosulphate. This free radical scavenging agent has a proven ability to minimize cochlear damage owing to the chemotherapeutic agent cisplatin. OBJECTIVES: This study had two distinct objectives. The first was to determine if sodium thiosulphate can reduce hearing loss in C57 mice concurrently subjected to gentamicin. The second goal was to assess the value of this animal model. METHODS: This study was accomplished by creating four treatment arms. The animals were provided with daily intraperitoneal injections of gentamicin (120 mg/kg), sodium thiosulphate (1600 mg/kg), gentamicin plus sodium thiosulphate, or normal saline. Auditory brainstem response threshold changes were calculated comparing differences between baseline values and those observed at day 35. RESULTS: The results indicate a trend suggesting that sodium thiosulphate may afford some degree of otologic protection when provided in conjunction with gentamicin. However, a statistical significance could not be established. Our mice appear to be more resistant to gentamicin-induced ototoxicity than found in previously reported animal models. CONCLUSION: We were unable to demonstrate that sodium thiosulphate can attenuate gentamicin-induced ototoxicity. Furthermore, we observe that the susceptibility to hearing loss varies considerably between individual C57 mice. Consequently, we hold some degree of reservation with the use of this model to assess the benefit of prospective rescue agents.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".