Predictors of Individual Response to Placebo or Tadalafil 5mg among Men with Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia: An Integrated Clinical Data Mining Analysis
Bibliographic record
Abstract
BACKGROUND: A significant percentage of patients with lower urinary tract symptoms (LUTS) secondary to benign prostatic hyperplasia (BPH) achieve clinically meaningful improvement when receiving placebo or tadalafil 5mg once daily. However, individual patient characteristics associated with treatment response are unknown. METHODS: This integrated clinical data mining analysis was designed to identify factors associated with a clinically meaningful response to placebo or tadalafil 5mg once daily in an individual patient with LUTS-BPH. Analyses were performed on pooled data from four randomized, placebo-controlled, double-blind, clinical studies, including about 1,500 patients, from which 107 baseline characteristics were selected and 8 response criteria. The split set evaluation method (1,000 repeats) was used to estimate prediction accuracy, with the database randomly split into training and test subsets. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) models were then generated on the training subset and used to predict response in the test subset. Prediction models were generated for placebo and tadalafil 5mg once daily Receiver Operating Curve (ROC) analysis was used to select optimal prediction models lying on the ROC surface. FINDINGS: International Prostate Symptom Score (IPSS) baseline group (mild/moderate vs. severe) for active treatment and placebo achieved the highest combined sensitivity and specificity of 70% and ~50% for all analyses, respectively. This was below the sensitivity and specificity threshold of 80% that would enable reliable allocation of an individual patient to either the responder or non-responder group. CONCLUSIONS: This extensive clinical data mining study in LUTS-BPH did not identify baseline clinical or demographic characteristics that were sufficiently predictive of an individual patient response to placebo or once daily tadalafil 5mg. However, the study reaffirms the efficacy of tadalalfil 5mg once daily in the treatment of LUTS-BPH in the majority of patients and the importance of evaluating individual patient need in selecting the most appropriate treatment.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".