The Impact of Chronic Exposure to Organophosphate Pesticides on the Incidence of Primary Brain Tumors in Farmers: A Narrative Review
Bibliographic record
Abstract
Highlight: Primary brain tumors have high mortality and morbidity rates. Organophosphate pesticides are commonly used by farmers. Chronic exposure to organophosphate pesticides can increase the incidence of primary brain tumors farmers. ABSTRACT Introduction: Primary brain tumors have high mortality and morbidity rates. Its causes and risk factors have not been explored further. Several studies indicate that exposure to pesticides can be one of the main triggering factors. Organophosphate insecticide is one of the most common pesticides used by farmers. Chronic exposure to organophosphates is known to trigger an imbalance of reactive oxygen species (ROS) in the body by suppressing the acetylcholinesterase enzyme. Objective: This study aimed to determine the impact of chronic exposure to organophosphates in terms of the types of organophosphates used by farmers, frequency of usage, and duration of exposure, using a narrative review method. Methods: A literature search was conducted with multiple electronic databases, such as PubMed, ScienceDirect, Cochrane Library, and Springer. The keywords will be searched using the boolean operator method, while synonyms will be found in the Medical Subject Heading (MeSH) database. Articles were assessed using the Newcastle-Ottawa Scale (NOS) Questionnaire. Results: There were 1071 articles found, but only 14 were selected for review. The studies showed that there was a relationship between the type, frequency, and duration of organophosphate usage and the incidence and risk factors for primary brain tumors. Conclusion: Chronic exposure to organophosphate pesticides, either directly or indirectly, can increase the incidence of primary brain tumors in farmers. The influencing factors include the type, frequency, and duration of pesticide exposure. The most influential factor is the duration of chronic pesticide exposure by farmers. Pesticide reduction exposure is beneficial in preventing the incidence of primary brain tumors in farmers.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".