Application of Generalized Additive Models to the Evaluation of Continuous Markers for Classification Purposes
Bibliographic record
Abstract
Background: Receiver operating characteristic (ROC) curve and derived measures as the Area Under the Curve (AUC) are often used for evaluating the discriminatory capability of a continuous biomarker in distinguishing between alternative states of health. However, if the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions, classification using a single cutpoint is not appropriate, and it would lead to erroneous conclusions. This study sought to describe a procedure for improving the discriminatory capacity of a continuous biomarker, by using generalized additive models (GAMs) for binary data. Methods: A new classification rule is obtained by using logistic GAM regression models to transform the original biomarker, with the predicted probabilities being the new transformed continuous biomarker. We propose using this transformed biomarker to establish optimal cut-offs or intervals on which to base the classification. This methodology is applied to different controlled scenarios, and to real data from a prospective study of patients undergoing surgery at a University Teaching Hospital, for examining plasma glucose as postoperative infection biomarker. Results: Both, theoretical scenarios and real data results show that when the risk marker-disease relationship is not monotone, using the new transformed biomarker entails an improvement in discriminatory capacity. Moreover, in these situations, an optimal interval seems more reasonable than a single cutpoint to define lower and higher disease-risk categories. Conclusions: Using statistical tools which allow for greater flexibility (e.g., GAMs) can optimize the classificatory capacity of a potential marker using ROC analysis. So, it is important to question linearity in marker-outcome relationships, in order to avoid erroneous conclusions.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".