All-cause and cause specific mortality in a cohort of 20 000 construction workers; results from a 10 year follow up
Bibliographic record
Abstract
BACKGROUND: Construction workers are potentially exposed to many health hazards, including human carcinogens such as asbestos, silica, and other so-called "bystander" exposures from shared work places. The construction industry is also a high risk trade with respect to accidents. METHODS: A total of 19 943 male employees from the German construction industry who underwent occupational health examinations between 1986 and 1992 were followed up until 1999/2000. RESULTS: A total of 818 deaths occurred during the 10 year follow up (SMR 0.71; 95% CI 0.66 to 0.76). Among those were 299 deaths due to cancer (SMR 0.89; 95% CI 0.79 to 1.00) and 312 deaths due to cardiovascular diseases (SMR 0.59; 95% CI 0.51 to 0.68). Increased risk of mortality was found for non-transport accidents (SMR 1.61; 95% CI 1.15 to 2.27), especially due to falls (SMR 1.87; 95% CI 1.18 to 2.92) and being struck by falling objects (SMR 1.90; 95% CI 0.88 to 3.64). Excess mortality due to non-transport accidents was highest among labourers and young and middle-aged workers. Risk of getting killed by falling objects was especially high for foreign workers (SMR 4.28; 95% CI 1.17 to 11.01) and labourers (SMR 6.01; 95% CI 1.63 to 15.29). CONCLUSION: Fatal injuries due to falls and being struck by falling objects pose particular health hazards among construction workers. Further efforts are necessary to reduce the number of fatal accidents and should address young and middle-aged, semi-skilled and foreign workers, in particular. The lower than expected cancer mortality deserves careful interpretation and further follow up of the cohort.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".