An Ototoxicity Grading System Within a Mobile App (OtoCalc) for a Resource-Limited Setting to Guide Grading and Management of Drug-Induced Hearing Loss in Patients With Drug-Resistant Tuberculosis: Prospective, Cross-Sectional Case Series
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) affects millions of people worldwide and is treated with medication including aminoglycosides and polypeptides. Individuals respond differently to medications as a result of their genetic inheritance. These differences in genetic inheritance can result in the underdosing or overdosing of medication, which may affect the efficacy or, in the case of aminoglycosides and polypeptides used in the treatment of all forms of TB, result in ototoxicity. When ototoxicity is detected, physicians should adjust dosages to minimize further ototoxicity and hearing loss; however, there are no suitable grading systems to define significant hearing loss. OBJECTIVE: The aim of this study was to develop a standardized grading system by making use of an electronic health (eHealth) platform to ensure that a user-friendly method was available to interpret hearing test results, calculate significant hearing loss, and provide recommendations with regard to dosage adjustments and management. It further aimed to establish the sensitivity of the newly developed grading scale. METHODS: This grading system was developed in South Africa based on data that were obtained from an audiology and pharmacokinetic study on patients with drug-resistant TB (DR-TB) at two DR-TB units at state-run hospitals. This feasibility study employed a prospective, cross-sectional, exploratory, descriptive case series research design, with a total of 22 participants. Participants underwent audiological and pharmacological assessments at baseline and every 2 weeks for the first 3 months of treatment. Various professionals (8 in total) were subsequently involved in the development of the eHealth system, including a software engineer, four audiologists, a pharmacist, a medical doctor, and a nurse. The app underwent 14 modifications that involved aspects of data storage, ease of usability, grades, and the risk factor checklist. RESULTS: An ototoxicity grading system within a mobile app for use by doctors, nurses, and audiologists was developed for patients with DR-TB. The purpose of this user-friendly ototoxicity calculator, OtoCalc, is to (1) assist health professionals in assessing patients for ototoxicity, (2) establish the clinical significance of ototoxicity by calculating the grade of hearing loss, (3) monitor the progression of hearing loss, and (4) enable systematic referral and management of patients according to their needs. CONCLUSIONS: This newly developed system is more sensitive than the existing grading methods for determining ototoxicity in patients with DR-TB. This app needs to be trialed in a larger sample to establish data security, ease of use, and suitability within this population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".