Notched Audiograms and Noise Exposure History in Older Adults
Notice bibliographique
Résumé
In Brief Objective: Using data from a population-based cohort study, we compared four published algorithms for identifying notched audiograms and compared their resulting classifications with noise exposure history. Design: Four algorithms: (1) Coles et al. (2000), (2) McBride and Williams (2001), (3) Dobie and Rabinowitz (2002), and (4) Hoffman et al. (2006) were used to identify notched audiograms. Audiometric evaluations were collected as a part of the 10-yr follow-up examinations of the Epidemiology of Hearing Loss Study, in Beaver Dam, WI (2003–2005, N = 2395). Detailed noise exposure histories were collected by interview at the baseline examination (1993–1995) and updated at subsequent visits. An extensive history of occupational noise exposure, participation in noisy hobbies, and firearm usage was used to evaluate consistency of the notch classifications with the history of noise exposure. Results: The prevalence of notched audiograms varied greatly by definition (31.7, 25.9, 47.2, and 11.7% for methods 1, 2, 3, and 4, respectively). In this cohort, a history of noise exposure was common (56.2% for occupational noise, 71.7% for noisy hobbies, 13.4% for firearms, and 81.2% for any of these three sources). Among participants with a notched audiogram, almost one-third did not have a history of occupational noise exposure (31.4, 33.0, 32.5, and 28.1% for methods 1, 2, 3, and 4, respectively), and approximately 11% did not have a history of exposure to any of the three sources of noise (11.5, 13.6, 10.3, and 7.6%). Discordance was greater in women than in men. Conclusions: These results suggest that there is a poor agreement across existing algorithms for audiometric notches. In addition, notches can occur in the absence of a positive noise history. In the absence of an objective consensus definition of a notched audiogram and in light of the degree of discordance in women between noise history and notches by each of these algorithms, researchers should be cautious about classifying noise-induced hearing loss by notched audiograms. Using data from a population-based cohort study (N = 2395), we compared four published algorithms for identifying notched audiograms and compared their resulting classifications with noise exposure history. The prevalence of notched audiograms varied greatly by definition. Among participants with a notched audiogram, almost one-third did not have a history of occupational noise exposure, and ∼11% did not have a history of noise exposure. These results suggest that there is a poor agreement across algorithms for audiometric notches. In addition, notches can occur without a positive noise history. Researchers should be cautious about classifying noise-induced hearing loss by notched audiograms.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».