Validation of 1997 Partin Tables' lymph node invasion predictions in men treated with radical prostatectomy in Montreal Quebec.
Bibliographic record
Abstract
OBJECTIVE: The accuracy of 1997 Partin Tables' lymph node invasion (LNI) predictions exhibits important variability in different testing populations. We explored the LNI predictive accuracy in radical prostatectomy (RP) patients from Montreal, Canada. Moreover, we assessed the extent of change in predictive accuracy related to a modification of PSA coding from categorical to continuous. METHODS: We used pretreatment serum PSA, clinical stage, and biopsy Gleason sum from 537 men treated with RP to compare predicted and observed rates of LNI. Accuracy was quantified with receiver-operating characteristics curves. RESULTS: Accuracy was 0.760 in 369 evaluable patients, when categorically coded pretreatment PSA (0-4, 4.1-10, 10.1-20, 20.1+) was combined with clinical stage and biopsy Gleason sum. A 2.7% accuracy increase was noted when categorically coded PSA was replaced with continuously coded values. CONCLUSION: Partin Tables' LNI predictions showed comparable accuracy to a community-based sample from the United States (0.766), and to a recent, multi-institutional sample (0.740). However, accuracy was lower than reported in internal (0.818), and external (0.837) academic, validation cohorts. Accuracy of LNI predictions was appreciably higher, when continuously coded PSA was used.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".