Geospatial access predicts cancer stage at presentation and outcomes for patients with breast cancer in southwest Nigeria: A population‐based study
Bibliographic record
Abstract
BACKGROUND: The majority of women in Nigeria present with advanced-stage breast cancer. To address the role of geospatial access, we constructed a geographic information-system-based model to evaluate the relationship between modeled travel time, stage at presentation, and overall survival among patients with breast cancer in Nigeria. METHODS: Consecutive patients were identified from a single-institution, prospective breast cancer database (May 2009-January 2019). Patients were geographically located, and travel time to the hospital was generated using a cost-distance model that utilized open-source data. The relationships between travel time, stage at presentation, and overall survival were evaluated with logistic regression and survival analyses. Models were adjusted for age, level of education, and socioeconomic status. RESULTS: From 635 patients, 609 were successfully geographically located. The median age of the cohort was 49 years (interquartile range [IQR], 40-58 years); 84% presented with ≥stage III disease. Overall, 46.5% underwent surgery; 70.8% received systemic chemotherapy. The median estimated travel time for the cohort was 45 minutes (IQR, 7.9-79.3 minutes). Patients in the highest travel-time quintile had a 2.8-fold increase in the odds of presenting with stage III or IV disease relative to patients in the lowest travel-time quintile (P = .006). Travel time ≥30 minutes was associated with an increased risk of death (HR, 1.65; P = .004). CONCLUSIONS: Geospatial access to a tertiary care facility is independently associated with stage at presentation and overall survival among patients with breast cancer in Nigeria. Addressing disparities in access will be essential to ensure the development of an equitable health policy.
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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 | 0.002 |
| 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) | 0.002 | 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".