Audiological Tests Used in the Evaluation of the Effects of Solvents on the Human Auditory System: A Mixed Methods Review
Bibliographic record
Abstract
This study aimed to scope the literature, identify knowledge gaps, appraise results, and synthesize the evidence on the audiological evaluation of workers exposed to solvents. We searched Medline, PubMed, Embase, CINAHL, and NIOSHTIC-2 up to March 22, 2021. Using Covidence, two authors independently assessed study eligibility, risk of bias, and extracted data. National Institute of Health Quality Assessment Tools was used in the quality evaluation of included studies; the Downs and Black checklist was used to assess the risk of bias. Of 454 located references, 37 were included. Twenty-five tests were studied: two tests to measure hearing thresholds, one test to measure word recognition in quiet, six electroacoustic procedures, four electrophysiological tests, and twelve behavioral tests to assess auditory processing skills. Two studies used the Amsterdam Inventory for Auditory Disability and Handicap. The quality of individual studies was mostly considered moderate, but the overall quality of evidence was considered low. The discrepancies between studies and differences in the methodologies/outcomes prevent recommending a specific test battery to assess the auditory effects of occupational solvents. Decisions on audiological tests for patients with a history of solvent exposures require the integration of the most current research evidence with clinical expertise and stakeholder perspectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".