0068 Elevated bone resorption predicts shorter recurrence-free survival (RFS) for bone metastasis in breast cancer (BC)
Bibliographic record
Abstract
Poster Session I. Predictive and prognostic factors S35histological grade 3 (60%), node-positive (n+) (46%), ly+ (59%), v+ (7%).Four subtypes based on expression profile of ER, PgR, HER2 were HR+HER2-(67%), HR+HER2+ (11%), HR-HER2+ (5%), HR-HER-(triple negative) (17%) (HR+:ER and/or PgR positive (more than 10% positive tumor cells by IHC), HR-:ER and PgR negative, HER2+:IHC 3+ or IHC 2+/FISH+).A total of 42, 24, 7 and 27% were treated with no therapy (nil), hormone alone (H), chemotherapy alone (C) and combination (C+H) in the adjuvant setting.With median follow-up of 78 months (range 5-186 months), 78 (35%) DFS events and 48 (22%) deaths have occurred.In univariate analysis, T3, grade 3, n+, ly+, v+, triple negative and chemotherapy alone were significant adverse factors associated with DFS and OS.There were no significant differences between patients aged under 35 and aged 35-39.In multivariate analysis, nodal status, ly, subtypes were independent prognostic factors for DFS and OS.With nodenegative (n0), ly was a significant and independent factor in univariate and multivariate analyses.With n0 and HR+, there were no significant differences between patients who were treated with H and C+H.With n0 and HR-, there were no significant differences between patients who were treated with C and nil. Conclusion:In young woman with node-negative breast cancer, ly was the most important prognostic factor and it was unclear whether adjuvant chemotherapy was effective.There is an urgent need to investigate tailored treatment according to more precise predictive factor.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".