Bibliographic record
Abstract
In biomedical research, biomarkers (diagnostic tests) are used in distinguishing healthy and diseased populations. The effectiveness and accuracy of a biomarker generally assessed through the use of a Receiver Operating Characteristic (ROC) curve model, and its functional such as area under the curve (AUC). The parametric (smooth) ROC curves are obtained under the specific distributions assumptions. A resulting ROC curve model is the plot of sensitivity versus 1-specificity for all possible threshold values. Most popular and widely used ROC curve model is bi-normal ROC curve model under the assumptions of normality. When the biomarker results are continuous and positively skewed (non-normal). The gamma distribution is supposed to a flexible model for positively skewed measurements. In practice use of bi-gamma ROC curve model is hindered by the fact that ROC function cannot be written in closed-form. The solution of the problem is to use transformed invariance property of ROC curve model. Which assumes that the test results of both diseased and healthy are normally distributed after some monotone transformation [1]. In this paper we propose a simple approximation solution for the problem mentioned in above lines using a normal approximation due to Wilson and Hilfertys [2]. Which is useful to approximate gamma distribution results with classical normal distribution based results.
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 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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".