Medical resource utilization (MRU) of abiraterone acetate plus prednisone (AAP) added to androgen deprivation therapy (ADT) in metastatic castration-naive prostate cancer: Results from LATITUDE.
Bibliographic record
Abstract
201 Background: AAP + ADT demonstrated significant improvements in overall survival and disease progression in the LATITUDE trial. The objective of this analysis was to assess event-driven MRU from AAP + ADT vs ADT alone. Methods: MRU data from the LATITUDE trial were obtained and consisted of medical utilization other than that mandated by protocol while patients were on treatment. MRU types included overnight hospitalizations and length of stay (LOS), emergency room (ER) visits, radiotherapy, surgery, imaging, and specialist and general practitioner (GP) visits. Rates by treatment (per 100 person-years) and rate ratios were estimated using zero-inflated Poisson regression. Difference in average LOS between treatment arms was assessed using repeated measures regression. Results: A total of 1199 patients were evaluated. Statistically significantly lower rates (24% reduction) of hospitalizations were observed with AAP + ADT compared with ADT alone (Table). The most common hospitalization reasons were bladder/urethral symptoms and infections, lung infections, and musculoskeletal/connective tissue pain. Average LOS per hospitalization episode was similar. Statistically significantly lower rates of imaging (40% reduction) and radiotherapy were also observed for AAP + ADT vs ADT alone. Rates for specialist visits, surgery ER visits, and GP visits were not statistically different. Conclusions: Adding AAP to ADT does not increase MRU and leads to lower rates of hospitalization, imaging, and radiotherapy. This likely reflects the more favorable clinical outcomes with AAP + ADT therapy. Clinical trial information: NCT01715285. [Table: see text]
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".