Impact of Routine Pathology Review on Treatment for Node-Negative Breast Cancer
Bibliographic record
Abstract
PURPOSE: Routine secondary pathology review influences diagnosis and treatment among patients diagnosed with breast cancer. The impact of review on patients with node-negative breast cancer and the nature of the pathology elements leading to management changes are not well described. METHODS: Patients with node-negative, invasive, or in situ breast cancer and evaluable nodes referred to the British Columbia Cancer Agency during two time periods between 2004 and 2007 were included. Pathologists with expertise in breast cancer reviewed the original reports and slides. Biomarker testing was not routinely repeated. Medical record review was conducted to determine whether original pathology was changed and whether recommended therapy was affected. RESULTS: Among 906 eligible patients, 405 (45%) received a pathology review. Univariate comparisons revealed that reviewed patients were younger (P < .001) and more likely to have close margins (P < .001), whereas other characteristics were similar. A total of 102 pathology changes were documented among 81 patients (20%). The most frequently changed elements were grade (40%) and lymphovascular (26%), nodal (15%), and margin (12%) status. These changes resulted in 27 treatment modifications among 25 patients (6%). Treatment changes were primarily related to nodal and margin status, and only two of 27 were related to measurement of tumor biology in women with estrogen receptor-positive, node-negative breast cancer. CONCLUSION: Reported rates of change are significant and warrant routine secondary pathology review among patients with node-negative breast cancer or ductal carcinoma in situ before final treatment is recommended. Review remains relevant in the era of gene expression signatures to determine margin and nodal status.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".