Automated Author ProfileMakaruse, Nyasha
Makaruse, Nyasha
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This systematic review addressed two questions: 1) For which audiometric test frequencies or pure tone averages are hearing threshold levels (HTLs) most susceptible to early occupational noise induced hearing loss (NIHL) before significant damage? 2) Which early flag metric best detects early hearing shifts due to noise for occupational NIHL surveillance? Systematic searches were conducted in Ovid MEDLINE(R) and Embase from July 2021 to May 2024. Eligibility was screened by two independent reviewers using Covidence. HTL results were analysed for susceptibility to noise-induced changes, and sensitivity and specificity of early flag metrics were assessed. Of 175 studies retrieved, 18 met the inclusion criteria. Ten studies emphasised the importance of testing at frequencies above 8 kHz, with HTLs at 12, 14, and 16 kHz frequently identified as the most noise susceptible. Conventional frequencies of 3-6 kHz were also noted as susceptible. NIOSH and OSHA metrics had low sensitivity and specificity, but modifications improved their performance to 100% sensitivity and 98% specificity. The review highlights the need to refine current metrics and explore extended high frequencies for NIHL monitoring. Research is required to determine frequencies for warning metrics and sensitive metrics for early occupational NIHL detection.
Authors
- Makaruse, Nyasha ;
- Maslin, Mike R. D. ;
- Campbell, Ziva Shai
This systematic review addressed two questions: 1) For which audiometric test frequencies or pure tone averages are hearing threshold levels (HTLs) most susceptible to early occupational noise induced hearing loss (NIHL) before significant damage? 2) Which early flag metric best detects early hearing shifts due to noise for occupational NIHL surveillance? Systematic searches were conducted in Ovid MEDLINE(R) and Embase from July 2021 to May 2024. Eligibility was screened by two independent reviewers using Covidence. HTL results were analysed for susceptibility to noise-induced changes, and sensitivity and specificity of early flag metrics were assessed. Of 175 studies retrieved, 18 met the inclusion criteria. Ten studies emphasised the importance of testing at frequencies above 8 kHz, with HTLs at 12, 14, and 16 kHz frequently identified as the most noise susceptible. Conventional frequencies of 3-6 kHz were also noted as susceptible. NIOSH and OSHA metrics had low sensitivity and specificity, but modifications improved their performance to 100% sensitivity and 98% specificity. The review highlights the need to refine current metrics and explore extended high frequencies for NIHL monitoring. Research is required to determine frequencies for warning metrics and sensitive metrics for early occupational NIHL detection.
Authors
- Makaruse, Nyasha ;
- Maslin, Mike R. D. ;
- Campbell, Ziva Shai