The Impact of Travel Distance on Cancer Stage at Diagnosis for Cancer: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Geographic access to healthcare services can impact cancer outcomes. This paper reviews and updates the current evidence and gaps in the literature on the associations between travel distance and cancer stage. METHODS: A search of electronic databases (PubMed, SpringerLink, and Science Direct) was conducted to identify studies published between 2015 and 2025. Studies examining the association between travel distance and cancer stage at diagnosis were included in this article. RESULTS: From 19,197 studies, 11 articles met the inclusion criteria. In summary, four articles reported significant associations between travel distance/time and cancer stage, while six other articles did not report any association. Significant associations were observed in sub-Saharan Africa. In contrast, studies from Scotland, Canada, and the United States did not show significant relationships, while results from Japan varied, with papers showing either no significant impact of travel distance or indicating a correlation with advanced stages. CONCLUSIONS: This study suggests that longer travel distance is associated with advanced cancer stage in countries with healthcare access challenges and highlights the importance of healthcare accessibility in improving early cancer detection.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".