International journal of audiology · 2026
From policy debate to empirical evidence: a proof-of-concept bibliometric and LLM-assisted abstract-level content analysis of OTC hearing aid research before and after FDA regulation.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mo Changgeng, Manchaiah Vinaya, Wasmann Jan-Willem A, Wang Shangqiguo
Аннотация
OBJECTIVE: To characterise temporal patterns in over-the-counter (OTC) hearing-aid research before and after U.S. Food and Drug Administration (FDA) implementation of the OTC category (effective October 2022), using bibliometrics and large language model (LLM)-assisted abstract-level annotation.
DESIGN: Bibliometric analysis combined with abstract-level LLM content analysis using the GABRIEL framework. The pipeline combined human screening, bibliometric analysis, GABRIEL-based abstract-level LLM annotation, and cross-model concordance assessment in a proof-of-concept workflow. Pre- and post-implementation cohorts were compared on eight predefined dimensions using Mann-Whitney U tests with Benjamini-Hochberg correction; cross-model concordance assessed robustness to model choice rather than criterion validity.
STUDY SAMPLE: 106 OTC hearing aid-related articles indexed in Web of Science, screened by human raters and divided into pre-implementation (n = 45, 1995-2022) and post-implementation (n = 61, 2023-2026) cohorts.
RESULTS: In the primary analysis, the only dimension reaching Benjamini-Hochberg-adjusted significance was reduced older-adult emphasis (p_adj = .050, r = .266); consumer perspective and mild-to-moderate hearing-loss focus increased directionally, with the consumer increase reaching adjusted significance only in an exploratory sensitivity analysis. Evidence type shifted from commentary-dominated to empirical research (χ2 = 13.32, Monte Carlo p = .014, Cramér's V = .385). Descriptive temporal patterns were consistent with reorientation beginning during the 2017-2022 legislative and rulemaking period rather than at market entry.
CONCLUSIONS: This proof-of-concept suggests that LLM-assisted abstract-level annotation, paired with human screening and bibliometric analysis, can support rapid mapping of research agendas in fast-evolving fields. Findings represent temporal associations within a high-precision Web of Science corpus rather than causal effects of the FDA rule or expert-validated content measurements. Persistent gaps in low-income/underserved population research and geographic asymmetries point to priorities for targeted investigation.
DESIGN: Bibliometric analysis combined with abstract-level LLM content analysis using the GABRIEL framework. The pipeline combined human screening, bibliometric analysis, GABRIEL-based abstract-level LLM annotation, and cross-model concordance assessment in a proof-of-concept workflow. Pre- and post-implementation cohorts were compared on eight predefined dimensions using Mann-Whitney U tests with Benjamini-Hochberg correction; cross-model concordance assessed robustness to model choice rather than criterion validity.
STUDY SAMPLE: 106 OTC hearing aid-related articles indexed in Web of Science, screened by human raters and divided into pre-implementation (n = 45, 1995-2022) and post-implementation (n = 61, 2023-2026) cohorts.
RESULTS: In the primary analysis, the only dimension reaching Benjamini-Hochberg-adjusted significance was reduced older-adult emphasis (p_adj = .050, r = .266); consumer perspective and mild-to-moderate hearing-loss focus increased directionally, with the consumer increase reaching adjusted significance only in an exploratory sensitivity analysis. Evidence type shifted from commentary-dominated to empirical research (χ2 = 13.32, Monte Carlo p = .014, Cramér's V = .385). Descriptive temporal patterns were consistent with reorientation beginning during the 2017-2022 legislative and rulemaking period rather than at market entry.
CONCLUSIONS: This proof-of-concept suggests that LLM-assisted abstract-level annotation, paired with human screening and bibliometric analysis, can support rapid mapping of research agendas in fast-evolving fields. Findings represent temporal associations within a high-precision Web of Science corpus rather than causal effects of the FDA rule or expert-validated content measurements. Persistent gaps in low-income/underserved population research and geographic asymmetries point to priorities for targeted investigation.