Application of bone scans for prostate cancer staging: Which guideline shows better result?
Bibliographic record
Abstract
INTRODUCTION: We evaluated the accuracy of current guidelines by analyzing bone scan results and clinical parameters of patients with prostate cancer to determine the optimal guideline for predicting bone metastasis. METHODS: We retrospectively analyzed patients who were diagnosed with prostate cancer and who underwent a bone scan. Bone metastasis was confirmed by bone scan results with clinical and radiological follow-up. Serum prostate-specific antigen, Gleason score, percent of positive biopsy core, clinical staging and bone scan results were analyzed. We analyzed diagnostic performance in predicting bone metastasis of the guidelines of the European Association of Urology (EAU), American Urological Association (AUA), and the National Comprehensive Cancer Network (NCCN) guidelines as well as Briganti's classification and regression tree (CART). We also compared the percent of positive biopsy core between patients with and without bone metastases. RESULTS: A total 167 of 806 patients had bone metastases. Receiver operating curve analysis revealed that the AUA and EAU guidelines were better for detecting bone metastases than were Briganti's CART and NCCN. No significant difference was observed between AUA and EAU guidelines. Patients with bone metastases had a higher percent positive core than did patients without metastasis (the cut-off value >55.6). CONCLUSION: The EAU and AUA guidelines showed better results than did Briganti's CART and NCCN for predicting bone metastasis in the enrolled patients. A bone scan is strongly recommended for patients who have a higher percent positive core and who meet the EAU and AUA guidelines.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".