Determinants Associated With Pesticide Exposure in Patients With Head and Neck Cancer: Protocol for a Systematic Review
Bibliographic record
Abstract
Background: Currently, head and neck cancer (HNC) associated with pesticide exposure represents a global public health concern. However, there is no consensus regarding the specific determinants involved in this association. Moreover, there is a lack of scientific evidence to support the development of systematic reviews on this topic. Objective: This study aims to synthesize the methodology for conducting a systematic review to explore the current scientific evidence on the determinants of HNC associated with pesticides. Methods: The review will follow the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines to ensure methodological rigor. The protocol includes detailed steps for constructing a robust search strategy using relevant databases such as PubMed, Embase, Scopus, Web of Science, CINAHL, LILACS, and AGRICOLA. Keywords and Medical Subject Headings terms related to "pesticides," "exposure," "head and neck neoplasms," and related concepts will be used to capture the most relevant studies. Eligibility criteria will be clearly defined, including study design (eg, cohort, case-control, and cross-sectional), population characteristics, exposure assessment, and cancer outcomes. Studies published in any language that involve human participants will be included. The studies will be screened in 2 phases: first by title and abstract and then by full-text review. Two independent reviewers will assess the quality of each study and extract key data, such as exposure levels, cancer subtypes, and effect sizes. Any disagreements will be resolved through discussion or by a third reviewer. Results: This systematic review was initiated in February 2025 after protocol registration. The literature search and review process is ongoing at the time of submission of this paper. Data extraction and quality assessment have not yet been completed. Final results of the review, including a synthesis of determinants associated with pesticide exposure in patients with HNC, are expected to be completed and submitted for publication in June 2026. Conclusions: The necessary steps to conduct a systematic review must be concise and publicly available to ensure replicability within the scientific community.
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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 | 0.097 | 0.118 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.020 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".