Systematic Scoping Literature Review of Disparities in Stage of Endometrial Cancer [A321]
Bibliographic record
Abstract
INTRODUCTION: Endometrial cancer (EC) in the United States is twice as lethal for Black than White people with a uterus (PWU), partly due to disparities in stage at diagnosis. This systematic scoping review utilizes the Pathway to Treatment framework to identify actionable care and evidence gaps. METHODS: The complete protocol was previously published (DOI 10.1186/s13643-021-01649-x). Structured searches of PubMed, EMBASE, Scopus, and Cochrane Central Register of Controlled Trials were performed. Two reviewers screened abstracts and then full articles for eligibility. Data meeting quality criteria were qualitatively synthesized. RESULTS: Out of 2,171 records, 24 were included, with 9 reporting on appraisal, 4 on help seeking, 5 on diagnostic, and 10 on pre-treatment intervals. Study quality ratings were heterogenous, between 3 and 9 per Newcastle-Ottawa, with a median score of 7.0. Based on 2,059 PWU from four studies, no disparities in EC-relevant health literacy were identified. Based on 2,991 PWU from one study, willingness of Black PWU to seek care for EC symptoms was either the same or greater than White PWU. Based on 3,006 PWU from two studies, the physician not disclosing concern for cancer may prolong the diagnostic interval. Based on 743,139 PWU from five studies, the pre-treatment interval is significantly (all P values<.05) longer for Black and Hispanic PWU, those with less insurance coverage, and those with lower socioeconomic status. CONCLUSION: The Pathway to Treatment framework operationalized the evidence, revealing when disparities in timeliness occur. Interventions to reduce delays in the diagnostic and pre-treatment Intervals are particularly promising for addressing EC stage disparities.
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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 | 0.040 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".