Treatment patterns of new metastatic castration-resistant prostate cancer (mCRPC) therapies: Real-world evidence from three datasets.
Bibliographic record
Abstract
228 Background: Little information exists regarding the sequences in which new mCRPC therapies with evidence of survival benefits are used. This study aims at describing the sequence of mCRPC medication use as observed in 3 healthcare datasets. Methods: Healthcare claims datasets (Dataset #1 and #2) and a community oncology electronic medical record (Dataset #3) were used to identify PC patients with ≥ 1 claim for a study drug (abiraterone acetate--AA, cabazitaxel--CAB, docetaxel – DOC, enzalutamide – ENZ, and sipuleucel T – SIP) occurring after 9/1/2012. The index date was the 1st study drug claim. Patients were excluded if a study drug claim occurred prior to 9/1/2012. Descriptive statistics summarized the proportion of patients receiving one vs. two or more lines of therapy. The prevalence of 1st line therapy and of 1st to 2nd-line sequences was analyzed. Results: Analysis of 3 unique datasets with > 5,900 PC patients revealed most patients received a single line of therapy. AA and DOC were the most common 1st line agents. The five most-prevalent 1st- to 2nd-line sequences identified in each database are shown in the table below. The most commonly observed 1st- to 2nd-line sequences were AA-ENZ, AA-DOC, and DOC-AA. Conclusions: Real world treatment selection for 5 mCRPC medications was consistent across 3 datasets. The majority of PC patients had a prescription/claim for a single agent. AA and DOC were the most commonly selected 1st line treatments. A 2nd-line agent was observed in 14-33% of patients. Similar patterns of 1st-2nd line sequences were observed between datasets. Further research is warranted with longer follow-up and consideration of other treatment interventions. [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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".