Evaluation of the Clinical Benefits of Nanoparticle Albumin-Bound Paclitaxel in Women with Metastatic Breast Cancer in British Columbia
Bibliographic record
Abstract
BACKGROUND: Altered formulations of taxanes may lack cross-resistance with standardly used solvent-based taxanes. The primary objective of the present study was to assess the clinical benefit of nanoparticle albumin-bound (nab)-paclitaxel in women with metastatic breast cancer previously treated with and without adjuvant taxane in British Columbia. METHODS: The BC Cancer Agency Pharmacy data repository and Breast Cancer Outcomes Unit database were linked to identify all patients who received nab-paclitaxel in British Columbia since its introduction in 2007. Hormone receptor status, demographic characteristics, number of cycles prescribed, and time to treatment failure were extracted and analyzed. RESULTS: From 2007 to 2011, 138 patients in British Columbia received nab-paclitaxel, with 122 patients available for analysis. Most (70.5%) received adjuvant chemotherapy; about a quarter (24.6%) received an adjuvant taxane. Patients who received adjuvant taxane were more likely to have node-positive (86.7% vs. 48.9%, p = 0.007), estrogen receptor-negative (46.7% vs. 13.0% p < 0.001) disease and to receive initial adjuvant radiotherapy (76.7% vs. 51.1%, p < 0.001). For the entire cohort, the median number of nab-paclitaxel cycles prescribed was 4.4 (range: 0.3-13). The median number of nab-paclitaxel cycles was greater when that agent was given as first- or second-line therapy than as third-line or greater therapy (5.0 cycles vs. 3.7 cycles respectively). The median time to treatment failure was 96 days in the prior adjuvant taxane group (range: 0-361) and 73.5 days in the no prior adjuvant taxane group (range: 0-1176). CONCLUSIONS: This retrospective study demonstrates potential clinical activity of nab-paclitaxel in metastatic breast cancer regardless of whether patients had prior exposure to adjuvant taxanes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".