PSA doubling time post radiation: the effect of neoadjuvant androgen ablation.
Bibliographic record
Abstract
OBJECTIVE: To determine whether men who relapse after neoadjuvant androgen ablation (NAA) and high-dose radiation therapy (RT) have faster PSA doubling times(PSAdt) than those who are treated with RT alone. MATERIALS AND METHODS: From a prospective database of 1880 patients treated with RT for localized prostate cancer, patients were selected for further study if they had a rising PSA profile >1 ng/ml, and were treated with either no NAA, or prolonged NAA (defined as 3-12 months NAA) with a minimum 5 years follow-up. ThePSAdt was calculated from the exponential line of best fit from the first post-nadir value >1 ng/ml to the last PSA prior to secondary intervention. Those patients with a rising PSA profile at 5 years of follow-up were further examined with linear regression to determine factors of possible independent adverse effect. RESULTS: There were 251 patients eligible with rising PSA profiles. Patients treated with NAA had higher pre-treatment Gleason scores (p<O.001), PSA (p<O.001), and T stage (p<0.001). Median duration of NAA was 5.1 months. Rising PSA profiles occurred in 78% of the RT-only group and 70% of the NAA group. In regression analysis,factors predictive of more rapid PSAdt were pre-treatment Gleason score (p<0.001), pre-treatment PSA(p=0.025), and T stage (p=0.017). The use of NAA(p=0.4) was not significant. CONCLUSION: The use of prolonged NAA in men treated with RT does not itself cause a more rapid PSAdt when relapse occurs. Faster relapse observed in these men is due to intrinsically more aggressive tumors prior to treatment.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".