Disparities in Timeliness of Endometrial Cancer Care
Bibliographic record
Abstract
OBJECTIVE: We use the person-centered Pathway to Treatment framework to assess the scope of evidence on disparities in endometrial cancer stage at diagnosis. This report is intended to facilitate interventions, research, and advocacy that reduce disparities. DATA SOURCES: We completed a structured search of electronic databases: PubMed, EMBASE, Scopus, ClinicalTrials.gov, and Cochrane Central Register of Controlled Trials databases. Included studies were published between January 2000 and 2023 and addressed marginalized population(s) in the United States with the ability to develop endometrial cancer and addressed variable(s) outlined in the Pathway to Treatment. METHODS OF STUDY SELECTION: Our database search strategy was designed for sensitivity to identify studies on disparate prolongation of the Pathway to Treatment for endometrial cancer, tallying 2,171. Inclusion criteria were broad, yet only 24 studies addressed this issue. All articles were independently screened by two reviewers. TABULATION, INTEGRATION, AND RESULTS: Twenty-four studies were included: 10 on symptom appraisal, five on help seeking, five on diagnosis, and 10 on pretreatment intervals. Quality rankings were heterogeneous, between 3 and 9 (median 7.2) per the Newcastle-Ottawa Scale. We identified three qualitative, two participatory, and two intervention studies. Studies on help seeking predominantly investigate patient-driven delays. When disease factors were controlled for, delays of the pretreatment interval were independently associated with racism toward Black and Hispanic people, less education, lower socioeconomic status, and nonprivate insurance. CONCLUSIONS: Evidence gaps on disparities in timeliness of endometrial cancer care reveal emphasis of patient-driven help-seeking delays, reliance on health care-derived databases, underutilization of participatory methods, and a paucity of intervention studies. SYSTEMATIC REVIEW REGISTRATION: Given that PROSPERO was not accepting systematic scoping review protocols at the time this study began, this study protocol was shared a priori through Open Science Framework on January 13, 2021 (doi: 10.17605/OSF.IO/V2ZXY), and through peer review publication on April 13, 2021 (doi: https://doi.org/10.1186/s13643-021-01649-x).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".