The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2713
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:13:23.557133Z
status: published
lens: p_space
sector: health
headline: Applying Machine Learning Models to Predict Occupational Noise-Induced Hearing Loss: A Systematic Review and Meta-analysis
dek: OBJECTIVES: To map and critically appraise prediction models for occupational noise-induced hearing loss (ONIHL) using machine learning (ML) approaches and to evaluate reporting quality and risk of bias. METHODS: Seven databases were systematically searched. Reporting quality was assessed using the TRIPOD-AI checklist, and risk of bias and applicability were evaluated using the PROBAST tool. A meta-analysis was performed to evaluate ML model performance for predicting ONIHL using the area under the curve (AUC),…
gain_title: (none)
problem_title: A systematic review of 16 studies on machine learning prediction of occupational noise-induced hearing loss found 56% at high risk of bias, none externally validated, and a pooled AUC of 0.788 for ML models versus 0.811 for logistic regression.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A systematic review of 16 studies on machine learning prediction of occupational noise-induced hearing loss found 56% at high risk of bias, none externally validated, and a pooled AUC of 0.788 for ML models versus 0.811 for logistic regression.
problem_evidence: PROBAST assessment revealed 56% (9/16) of studies at high risk of bias | none of the studies performed external validation | Meta-analysis of 29 ML models yielded a pooled AUC of 0.788
quick_read: A systematic review and meta-analysis in Noise and Health pooled 16 studies, covering 29,094 workers, that used machine learning to predict occupational noise-induced hearing loss. Across 29 ML models the pooled AUC was 0.788, versus 0.811 for six logistic regression models.

Nine of the 16 studies (56%) were rated at high risk of bias with the PROBAST tool, and 31% had high applicability concerns. All 16 relied on internal validation only, so the reported accuracy may not carry over to other workplaces. The authors conclude the evidence should be read with caution.

XGBoost and artificial neural networks scored highest in subgroup analysis (both 0.846), but with very high heterogeneity (I2 of 94.4% and 95.8%). The definition of hearing loss was a major source of the differences between studies.
limitation: Heterogeneity was not fully explained by subgroup analyses, the number of studies per algorithm was small, and no included study was externally validated.
tag: Evidence-backed problem
key_points: Seven databases were searched; 16 studies from 2015 to 2025 were included, covering 29,094 participants, mostly middle-aged men in Asian industries such as manufacturing and mining. | Support vector machines were used in nine studies, random forests in seven, artificial neural networks in six, and decision trees and XGBoost in five each; only one study used a convolutional neural network. | Age, noise level and exposure duration were the most common inputs; noise features such as kurtosis were rarely used. | PROBAST rated 56% of studies high risk, 38% low and 6% unclear; the analysis domain was the main problem, with 44% rated high risk. | Pooled AUC was 0.788 for 29 ML models and 0.811 for six logistic regression models, both with high heterogeneity (I2 of 93.4% and 87.4%). | Studies defined hearing loss in at least seven different ways; heterogeneity was 0% for one definition and 95.8% for another. | Average TRIPOD-AI reporting compliance across the 16 studies was 64%.
rundown: The review followed PRISMA. From 2147 records, 1542 were screened after duplicates were removed, and 16 observational studies were included. Reporting quality was checked against the 27-item TRIPOD-AI checklist, with average compliance of 64%, and bias was assessed with PROBAST.

Pooled discrimination was similar for ML and logistic regression, with AUCs of 0.788 and 0.811. Individual studies often reported ML beating traditional models, including the ISO-1999 standard, but the pooled estimates did not show that advantage. In subgroups, XGBoost and ANN reached 0.846, KNN 0.837, random forest 0.808, SVM 0.792, decision trees 0.781 and AdaBoost 0.745.

The authors attribute bias mainly to small samples, too few events per predictor variable and weak handling of overfitting. All studies used hold-out or k-fold cross-validation; none validated on an independent cohort. They call for external validation, standard hearing-loss definitions and deep learning on noise time-domain features.
sources:
- peer_reviewed | Noise and Health | https://doi.org/10.4103/nah.nah_11_26 | 2026-05-01
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

1c20cbf7b01b79bc667ff52ba9e2d278350768c81d210820fef1ec085736c8eb

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000