The relationship between metabolites of polycyclic aromatic hydrocarbons in urine and changes in cognitive function of steel mill workers：a longitudinal study
Bibliographic record
Abstract
ObjectiveTo investigate the relationship between the metabolites of polycyclic aromatic hydrocarbons (PAHs) in urine and the changes in cognitive function of steel mill workers. MethodsIn 2019, 960 workers from a coking plant in Shanxi province were selected to undergo basic information investigations, cognitive function tests, and detection for hydroxylated metabolites of polycyclic aromatic hydrocarbons (OH-PAHs) in urine. During the follow-up in 2023, a total of 567 workers completed the cognitive function tests. The Beijing version of the Montreal Cognitive Assessment (MoCA) was employed to test the cognitive function of the workers. High performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS) was utilized to detect OH-PAHs in urine. The generalized linear model and the generalized estimation equation were adopted to analyze the relationship between urinary OH-PAHs and the cognitive function of the workers. ResultsSubjects were 49 (46, 53) years old at baseline, with 91.7% being the male. In the longitudinal study, the high exposure groups of 2-hydroxynaphthalene (2-OHNAP) and 1-hydroxypyrene (1-OHPYR) showed decreases in attention (β = –0.338, 95%CI: –0.454 to –0.222, P < 0.001; β = –0.450, 95%CI: –0.557 to –0.343, P < 0.001), abstract thinking (β = –0.463, 95%CI: –0.549 to –0.377, P < 0.001; β = –0.360, 95%CI: –0.450 to –0.269, P < 0.001) and the total score (β = –0.863, 95%CI: –1.284 to –0.443, P < 0.001; β = –0.548, 95%CI: –0.987 to –0.108, P = 0.015). ConclusionsLong-term exposure to high concentrations of PAHs can lead to reduced cognitive function in steel mill workers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".