Ethnic disparities in the incidence of gynecologic malignancies among Israeli Women of Arab and Jewish Ethnicity: a 10-year study (2010–2019)
Bibliographic record
Abstract
Background: Ethnic disparities in healthcare outcomes persist, even when populations share the same environmental factors and healthcare infrastructure. Gynecologic malignancies are a significant health concern, making it essential to explore how these disparities manifest in terms of their incidence among different ethnic groups. Objective: To investigate ethnic disparities in the incidence of gynecologic malignancies incidence among Israeli women of Arab and Jewish ethnicity. Design: Our research employs a longitudinal, population-based retrospective cohort design. Method: Data on gynecologic cancer diagnoses among the Israeli population from 2010 to 2019 was obtained from a National Registry. Disease incidence rates and age standardization were calculated. A comparison between Arab and Jewish patients was performed, with Poisson regression models being used to analyze significant rate changes. Results: Among Jewish women, the age-standardized ratio (ASR) for gynecologic malignancies decreased from 288 to 251 ( p < 0.001) between 2014 and 2019. However, there was no significant change in the ASR among Arab women during the same period, with rates going from 192 to 186 ( p = 0.802). During the study period, the incidence of ovarian cancer decreased significantly among Jewish women ( p = 0.042), while the rate remained stable among Arab women ( p = 0.102). A similar trend was observed for uterine cancer. The ASR of CIN III (Cervical Intraepithelial Neoplasia Grade 3) in Jewish women notably increased from 2017 to 2019, with an annual growth rate of 43.3% ( p < 0.001). A similar substantial rise was observed among Arab women, with an annual growth rate of 40.5% ( p < 0.001). In contrast, the incidence of invasive cervical cancer remained stable from 2010 to 2019 among women of both ethnic backgrounds. Conclusion: Our findings indicate that Arab women in Israel have a lower incidence rate of gynecologic cancers, warranting further investigation into protective factors. Both ethnic groups demonstrate effective utilization of cervical screening.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".