Evaluation of a Model of Long-Term Middle Ear Catheterization for Repeat Infusion Administration and Cochlear Hair Cell Injury in Guinea Pigs
Bibliographic record
Abstract
Middle ear administration has numerous applications, including antibiotherapy and gene therapy, and is increasingly used to target the auditory and vestibular systems. In animal studies, investigating repeated exposure that mimics clinical dosing regimens has remained a challenge due to the lack of suitable models. Intratympanic injections are not suitable for long-term studies due to the increased risk related to tympanic membrane rupture or scarring and repeat anesthesia events. Surgical models of middle ear catheterization previously used have not been reliable for longer than 4 weeks, resulted in elevated stress levels, and have been associated with significant changes related to the surgery and/or the presence of the catheter such as local trauma and inflammatory and degenerative processes. These complications cause decreased hearing/deafness and greatly diminish the value and accuracy of ototoxicity studies. We describe here a procedure that permits repeat dosing into the middle ear of guinea pigs and can be used to produce a model of aminoglycoside-induced hair cell injury. The innocuity of the procedures and the efficacy of the ototoxicity model were confirmed using auditory brain stem response assessment, histopathological evaluation, and cytocochleograms. Procedure-related changes were limited to minimal inflammation in the middle ear.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".