[Meta-analysis of association between pentachlorophenol exposure and cancer risk].
Bibliographic record
Abstract
OBJECTIVE: The epidemiological data based on the exposure of pentachlorophenol (PCP) and cancer incidence and mortality were analyzed to study the relationship between PCP exposure and cancer risk. METHODS: According to the online search of relevant literatures, Poisson regression was used to analyze mortality rates for major cancer sites and fixed-effect model was employed to assess cancer SMR. The dose-response relationship between PCP exposure and cancer risk was also analyzed. RESULTS: Major cancer mortality rates of exposure populations researched in American and Canadian studies were approximate to or lower than national male cancer mortality rates respectively. The incidence rate of colorectal cancer in occupational exposure population was over 16.4 times in comparison with the population exposed from to drinking water and food. The pooled SMR value of kidney cancer was 1.34 (95% CI 1.02-1.77). The pooled RR for major cancer mortality increased with the rise of PCP exposure level. CONCLUSION: A potential dose-response relationship between PCP exposure and cancer risk could exist. In comparison with to the population to exposed from the drinking water and food, the risk of colorectal cancer in occupational exposure population was higher. PCP may be one of the risk factors of the kidney cancer in occupational exposure population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".