REGIONAL RURAL DIFFERENCES IN THE EPIDEMIOLOGICAL CHARACTERISTICS OF THE MENSTRUAL CYCLE (MC), ACCORDING TO AGROCHEMICAL (AC) USE, IN A COUNTRY OF LATIN AMERICA.
Bibliographic record
Abstract
Background and Aims: To describe for first time, the characteristic of menstrual cycle (MC) in a Colombian group of rural fertile women and evaluate if there are differences according rural geographic regions with different use of agrochemicals. Methods: An ecological study of menstrual cycle characteristics (menarche, cycle length, bleeding length and variability) according physical and sociodemographic characteristics in 1603 Colombian rural fertile women from five rural regions with different use of agrochemicals, was performed. Results: Age mean was 22.4 (SD 4.4) years, minimal 15 and maxim 54. Mean of cycle length was 31.3 (SD11.4) with a range between 12 to 180 days, bleeding length had a mean of 4.7 (SD 2.0), range 1-47 days, and menarche installation had a mean of 13.0 (SD1.4), range 8-18 years. Distribution of cycle length in different regions doesn’t offer great differences but proportion of women with variability was 11.7% and was highest in the region with most intensive use of agrochemicals products cause of illicit crops (21.6%). Regional differences in the menarche age were founded. Conclusion: It is the first time that a community population based study is performed in order to know general characteristics of MC in a country with special use of agrochemicals. Regional differences were strong but it is impossible conclude that them have been caused only for use of AC products, but this study sheds light to geographical areas where is necessary to make other kind of complementary studies and present a basic information for subsequent studies related with MC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".