131. MANAGMENT OF BREAST CANCER TREATMENT–INDUCED BONE LOSS
Bibliographic record
Abstract
Background: Breast cancer treatment therapies are associated with early menopause and adverse effect on bone density. Identifying high-risk patients and managing them appropriately will help reduce their fracture risk. We wanted to ascertain whether our management of breast cancer patients referred to the osteoporosis clinic was as per the national and international guidelines. A consensus position statement from a UK Expert group regarding breast cancer treatment induced bone loss was published with the support of the National Osteoporosis Society, the National Cancer Research Institute, Breast Cancer Study Group and the International Osteoporosis Foundation. Methods: Data were collected from 1 June 2014 to 31 August 2016 for all patients referred to the osteoporosis clinic that had a history of or were presently being treated for Breast cancer. All patients underwent a DEXA scan prior to consultation in clinic. Patients were placed into two algorithms; algorithm 1: women who had experienced premature menopause due to chemotherapy or ovarian suppression, ablation or removal. Algorithm 2: Postmenopausal women receiving treatment with an aromatase inhibitor. T-scores were used to stratify patients in algorithm 1 into high (T score ≤−2), medium (T score −1 to −2) and low risk (T score ≥−1) and algorithm 2 into high (T score ≤−1) and medium risk (T score ≥−1).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.011 | 0.002 |
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".