A critical appraisal of logistic regression‐based nomograms, artificial neural networks, classification and regression‐tree models, look‐up tables and risk‐group stratification models for prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. METHODS: We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. RESULTS: Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. CONCLUSION: These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".