Impact of geography on receipt of medical oncology consultation and neoadjuvant chemotherapy for triple negative and HER2 positive breast cancer.
Bibliographic record
Abstract
1530 Background: Consensus guidelines recommend consideration of neoadjuvant chemotherapy (NAC) for most patients with early-stage triple negative (TN) and HER2 positive (HER2+) breast cancer. Based on our previous work, most patients are not seen by a medical oncologist prior to surgery and do not receive NAC. Although potential barriers are not well understood, distance to cancer centres has been shown to mediate medical oncology referral and use of systemic therapy for other malignancies. We therefore aimed to characterize the impact of region of residence and cancer centre proximity on receipt of pre-treatment medical oncology consultation and NAC for patients with TN and HER2+ breast cancer. Methods: Using linked administrative healthcare datasets in Ontario, Canada, we performed a retrospective population-based geographic analysis of women diagnosed with stage I-III TN or HER2+ breast cancer from 2012-2019. Outcomes were pre-treatment medical oncology consultation and initiation of NAC. We created choropleth maps to assess the spatial distribution of outcomes across census divisions, displaying cancer centres and outcomes by colour gradient using geographic information system analysis. To assess the relationship between distance to the nearest cancer centre and outcomes, we performed a multivariable regression analysis adjusted for sociodemographic and clinical factors, including tumour extent and nodal status. Results: Within the cohort of 12,881 patients, there was no statistically significant relationship between incremental distance to the nearest cancer centre (≤5 vs. 5-10, 10-25, and ≥25 km) and rate of medical oncology consultation or receipt of NAC. Mapping analysis demonstrated high interregional outcome variability, ranging across census divisions from 12.2% to 64.3% for medical oncology consultation rate, and 9.3% to 64.3% for NAC rate. 77.7% of patients referred to medical oncology received NAC. Conclusions: Among patients with TN and HER2+ early breast cancer, rates of medical oncology consultation and NAC were not significantly impacted by distance to cancer centres but varied highly by region. This variability suggests that regional and/or provider practice patterns, rather than distance, may underlie discrepancies in referral for NAC. These findings can inform further studies and interventions aiming to improve equitable access to NAC for patients with TN and HER2+ breast cancer.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".