FR01-01 SPAWN OF THE DEAD: A HISTORY OF POSTMORTEM SPERM RETRIEVAL
Bibliographic record
Abstract
date were identified from 01 April 2014 to 31 March 2017.Patients had continuous VHA enrollment for !12 months pre-and post-index date and were followed until death or disenrollment.Kaplan-Meier analysis was conducted to estimate OS and Cox proportional hazards regression models examined the impact of treatment on survival.Patients initiating abiraterone acetate were 1:1 propensity score matched (PSM) with those initiating enzalutamide.All-cause and PCrelated resource use and costs per-patient-per-month (PPPM) were compared between the matched cohorts during the 12 months postindex date.RESULTS: This study included 1,945 abiraterone acetate and 1,229 enzalutamide mCRPC patients with mean ages of 73 and 74 years respectively.After a median follow-up of 18 months and 19 months, enzalutamide and abiraterone acetate patients had a median survival time of 30 months and 26 months, respectively.In the Cox analysis, enzalutamide patients had better survival compared to abiraterone acetate patients (HR[0.87;95%CI 0.78-0.96).After PSM, there were 1,160 patients left in both cohorts.Compared to abiraterone acetate patients, enzalutamide patients had fewer mean all-cause outpatient visits PPPM (2.51 vs 2.86; p<.0001) and fewer mean PC-related outpatient visits PPPM (0.86 vs 1.03; p<.0001).Enzalutamide patients also had lower mean all-cause outpatient costs PPPM ($2,588 vs $3,115; p<.0001) and mean total costs PPPM ($8,085 vs $9,092; p[.0002); lower mean PC-related outpatient costs PPPM ($1,356 vs $1,775; p<.0001) and mean total costs PPPM ($6,321 vs $7,280; p<.0001) than abiraterone acetate patients.CONCLUSIONS: Chemotherapy-naive mCRPC patients treated with enzalutamide had better survival, significantly lower resource use and healthcare costs than patients treated with abiraterone acetate.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".