The Current Practice of Screening, Prevention, and Treatment of Androgen-Deprivation-Therapy Induced Osteoporosis in Patients with Prostate Cancer
Bibliographic record
Abstract
Introduction. ADT is used in the management of locally advanced and metastatic disease. The detrimental effect of ADT on bone density is well documented. This study assesses care gaps in screening, prevention and treatment of osteoporosis among prostate cancer patients. Methods. We conducted a retrospective cohort study for patients diagnosed with non-metastatic prostate cancer on ADT. Charts from a tertiary oncology center were assessed for utilization of DXA scan, prescription of calcium, vitamin D, calcitonin and bisphosphonates.Bivariate analysis was used to determine the effect of patient characteristics and likelihood for osteoporosis screening. Results. 149 charts were reviewed, with 3-year mean follow-up. 58.8% of men received a baseline DXA, of which 20.3% had a repeat DXA within their follow-up periods.In all, 28% were appropriately screened and managed for osteoporosis (received repeat DXA, bisphosphonate). In bivariate analysis, the number of ADT injections which correlate with the duration of androgen suppression was significantly associated with the number of DXA scans. Conclusions. Our study found a care gap in the screening, prevention, and treatment of osteoporosis in this population. Patients receiving the most ADT injections were more likely to be screened. Our results suggest healthcare providers treating prostate cancer are insufficiently screening and treating this susceptible population. We suggest baseline measurement of BMD at the initiation of ADT with periodic reassessment during therapy.
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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".