Bibliographic record
Abstract
Introduction: prostate-specific membrane antigen (psma) demonstrates overexpression in prostate cancer and correlates with tumor aggressiveness.psma positron emission tomography (pet) has been shown to be superior to conventional imaging for the metastatic staging of prostate cancer.the objective of this study was to determine the accuracy of 18 F-psma-1007 pet/ct compared to multiparametric magnetic resonance imaging (mrI, the current gold standard) in the primary locoregional staging of intermediate-and high-risk prostate cancers.Methods: the next generation trial (nct05141760) was a phase 2, prospective, validating, paired-cohort trial assessing 18 F-psma-1007 pet/ct and mrI for locoregional staging of prostate cancer, with final histopathology as the gold-standard comparator in 134 patients undergoing prostatectomy.radiologists, nuclear medicine physicians, and pathologists were blinded to preoperative clinical, pathology and imaging data.the primary outcome was correct identification of the prostate cancer tumor ('t') stage.the secondary outcomes were correct identification of the dominant nodule, laterality, extracapsular extension, and seminal vesical invasion.Results: psma pet was superior to mrI for the accurate identification of the final pathologic t stage (45% vs. 28%, p=0.003).psma pet was also superior to mrI for the correct identification of the dominant nodule (94% vs. 83%, p=0.007), laterality (64% vs. 44%, p=0.001), and extracapsular extension (75% vs. 63%, p=0.014), but not for seminal vesicle invasion (91% vs. 85%, p=0.065).Conclusions: In this trial, 18 F-psma-1007 pet/ct was superior to mrI for the locoregional staging of prostate cancer.these findings support the use of psma pet in the preoperative workflow of intermediate-and high-risk tumors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.656 | 0.482 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".