Evidence map›Paper›PMID 42812435›Full record

ArticleFrontiers in rehabilitation sciences2026

Statistical interval-based assessment of work disability using large-scale medical data: implications for public health and workforce participation.

Lyazat M Aktayeva, Aiman S Iskakova

Abstract read
In one paragraph

Article in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Lyazat M AktayevaRepublican Research Institute for Occupational Safety and Health, Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan, Astana, Kazakhstan.
Aiman S IskakovaRepublican Research Institute for Occupational Safety and Health, Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan, Astana, Kazakhstan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Work disability assessment is a key component of occupational health systems, influencing compensation, return-to-work decisions, and workforce management. However, variability in expert evaluations remains a major challenge. This study aims to identify recurring population-level patterns of disability severity using large-scale medical expert data. Methods: A retrospective dataset of 4,863 disability assessments conducted between 2020 and 2024 was analyzed. After applying inclusion criteria, 2,065 observations across 161 ICD-10 diagnostic categories were included. Statistical analysis involved empirical distribution modeling, concentration function estimation, confidence interval analysis, and hypothesis testing. Robustness was assessed using Monte Carlo resampling. Sensitivity analyses indicated that the predefined intervals were frequently reproduced as dominant concentration intervals under repeated resampling. Results: Disability severity showed distinct concentration patterns across diagnostic groups. Moderate impairment cases were concentrated within the 30%-45% interval, while more severe cases clustered within the 46%-59% interval. These intervals captured more than 90% of observations in the major diagnostic groups. Differences in disability distributions between diagnostic groups were statistically significant ( Conclusions: The proposed interval-based approach provides a reproducible, data-driven representation of work disability severity. Rather than demonstrating improved consistency in expert assessment, the findings identify recurring concentration patterns that may provide a quantitative reference for further research on disability assessment. Further external and clinical validation is required before the approach can be considered for implementation in disability assessment practice. These findings are relevant for population-level assessment of work disability and may inform public health monitoring and workforce participation policies.

Indexed as

disability assessmentfunctional impairmentoccupational healthwork disabilityworkforce participation

Identifiers

PMID42812435
PMCPMC13619917

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.