患有骨质发育不完善的个体中听力学测试的频率和改善听力仪表参与的建议
Holly LoTurco1, Chloe Derocher1, Su Htwe1
1*Hospital for Special Surgery, New York, NY.
Journal of the American Academy of Audiology
|May 27, 2025
概括
骨质发育不完善 (OI) 患者经常有听力损失和不频繁的听力学测试. 便携式听力测量设备可以改善早期检测和照顾OI个体的听力损伤.
科学领域:
- 听力学 听力学是指听力学.
- 遗传学 是一个遗传学.
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 骨质变生不完美 (OI) 是一种遗传疾病,与骨脆弱和骨折风险增加有关.
- 患有OI的人通常比一般人群更早地经历听力损失.
- 对OI患者来说,早期发现和管理听力障碍至关重要.
研究的目的:
- 确定患有骨质发育不完善症 (OI) 的患者听力学测试的频率.
- 评估便携式听力测试设备在改善参与听力测试方面的有效性.
- 评估OI人群中听力损失的流行程度.
主要方法:
- 对97名参与者进行了前性观察性研究.
- 参与者使用SHOEBOX Audiometry Pro系统进行了一次听力学测试.
- 听力损失被定义为在任何测试频率下≥25dB的纯音值值 (PTT).
主要成果:
- 大多数参与者 (54/97) 报告说,听力学检查的频率不高于每两年一次.
- 测试不频繁的主要原因是患者没有感知到听力问题.
- 观察到听力损失的显著流行率,71% (69/97) 的参与者在一个或多个频率下具有PTT ≥25dB.
结论:
- 在OI诊所实施便携式听力测量可以提高早期识别听力损失.
- 改进的听力学测试有助于及时对OI患者听力损失的随访护理.
- 早期干预可以显著改善OI和听力损失患者的生活质量.
更多相关视频
09:07Author Spotlight: Advancing Endoscopic Ossiculoplasty – Techniques, Innovations, and Practical Guidance for Clinical Integration
Published on: January 26, 2024
2.6K
03:49Author Spotlight: Optimizing EAS with Long Electrodes for Enhanced Cochlear Coverage and Hearing Preservation
Published on: October 11, 2024
925
相关概念视频
The Auditory Ossicles
2.0K
The auditory ossicles of the middle ear transmit sounds from the air as vibrations to the fluid-filled cochlea. The auditory ossicles consist of two malleus (hammer) bones, two incus (anvil) bones, and two stapes (stirrups), one on each side. These bones develop during the fetal stage and are the ones to ossify first. They are fully mature at birth and do not grow afterward.
The aptly named stapes look very much like a stirrup. The three ossicles are unique to mammals, and each plays a role in...
The aptly named stapes look very much like a stirrup. The three ossicles are unique to mammals, and each plays a role in...
2.0K
Expected Frequencies in Goodness-of-Fit Tests
2.7K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.7K
