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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».