The Breast to Bone (B2B) Cohort Study to Prevent, Detect and Improve Treatment of Metastatic Disease: Baseline Assessment, Description and Progress
Bibliographic record
Abstract
Women diagnosed with early-stage breast cancer can develop metastatic disease following successful initial treatment, with bone being the most common site of metastases. The Breast to Bone (B2B) cohort study of early-stage breast cancer patients was established as a research platform to study the basic biology of breast cancer to bone metastasis and to identify the factors that could improve prevention, early detection and treatment for this debilitating and incurable disease. The B2B cohort includes 478 women diagnosed with incident primary breast cancer (stages I to III) who were recruited from Calgary, Alberta and surrounding areas between February 2010 and July 2015. Four projects have been conducted to date, utilizing data and samples from this cohort. These studies have found the following: (a) women with insufficient or deficient levels of vitamin D (25[OH]D) concentrations in pretreatment serum samples had larger tumors and higher breast cancer grades, (b) several metabolomic biomarkers and cytokines were associated with tumor characteristics and time to recurrence, (c) additional biomarkers were found to be predictive for the high risk of bone metastasis and (d) circulating progastrin (hPG80) was associated with multiple survival outcomes. These research studies and future ones will provide new evidence on bone metastasis etiology in women diagnosed with early-stage breast cancer, improve identification of those at high risk and contribute to personalized treatment and prevention options.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".