North American population-based validation of the National Comprehensive Cancer Network Practice Guideline Recommendations for locoregional lymph node and bone imaging in prostate cancer patients
Bibliographic record
Abstract
BACKGROUND: The National Comprehensive Cancer Network (NCCN) guidelines provide recommendations for staging of prostate cancer patients in the objective regarding presence of locoregional lymph node metastases (LNM) and bone metastases. We tested the performance characteristics of these recommendations in a community setting. METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2014), we identified patients with available Gleason, clinical stage and prostatic specific antigen. Performance characteristics endpoints consisted of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NVP), overall accuracy and the number needed to image (NNI). RESULTS: Totally, 191,308 patients were assessable for the validation of the LNM staging recommendations. Sensitivity ranged from 80.6 to 86.3%, specificity from 74.7 to 79.3%, PPV from 7.8 to 8.0%, overall accuracy from 75.0 to 79.3% and NPV was 99.5%. The respective NNI values were 12.5 and 12.8. 197,408 patients were assessable for the validation of bone scan recommendations. These recommendations resulted in 90.8% sensitivity, 76.3% specificity, PPV of 5.7%, NPV of 99.8% and overall accuracy of 76.5%. The NNI was 17.5. CONCLUSION: The NCCN recommendations for locoregional LNM miss few patients with clinical LNM (0.3-0.4%) and provide a virtually perfect NPV of 99.5%. Also, the recommendations for bone scan miss a marginal number of patients with established bone metastases (0.14%) and yield a virtually perfect NPV of 99.8%.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".