Ototoxicity Induced by Gentamicin and Furosemide
Bibliographic record
Abstract
OBJECTIVE: To present a case of ototoxicity induced by furosemide and once-daily gentamicin therapy. CASE SUMMARY: A 60-year-old white woman presented to the hospital with community-acquired pneumonia and urinary tract infection. The antibiotic regimen included gentamicin and, after 5 doses, the patient reported profound bilateral hearing loss. A Pure Tone Audiogram suggested moderate to moderately severe sensorineural hearing loss bilaterally. The only risk factors present included her age, elevated temperature, and the use of furosemide. DISCUSSION: Several risk factors may predispose a patient to developing aminoglycoside ototoxicity: the 1555 chromosomal mutation, preexisting disorders of hearing and balance, hypovolemia, bacteremia, liver and renal dysfunction, and the simultaneous administration of other ototoxic medications. The cumulative dose and duration of aminoglycoside therapy are more important than serum concentrations. Administration of an aminoglycoside followed by furosemide may increase the risk of ototoxicity. The aminoglycoside interacts with the cell membranes in the inner ear, increasing their permeability. This theoretically allows the loop diuretic to penetrate into the cells in higher concentrations, causing more severe damage. CONCLUSIONS: Auditory toxicity occurred after only 5 days of gentamicin therapy and 1 dose of furosemide. An aminoglycoside followed by furosemide may increase the risk for ototoxicity. Clinicians need to be aware of the synergistic potential of ototoxic medications.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".