The diagnostic utility of multiple-level likelihood ratios
Bibliographic record
Abstract
Clinicians are accustomed to interpreting diagnostic test scores in terms of sensitivity and specificity. Many clinicians also appreciate that sensitivity and specificity need to be interpreted in terms of local base rates (i.e., pretest probability). However, most neuropsychological tests contain a wide range of scores. Important diagnostic information may be sacrificed when valid test scores are reduced to the simple dichotomy of "positive" or "negative" diagnosis that underlies sensitivity and specificity analysis. The purpose of this study is to provide an introduction to multiple-level likelihood ratios, a method for preserving the information in a wider range of scores. These statistics are first described using a hypothetical example of dementia screening, then with patient data from an epilepsy surgery sample. Multiple-level likelihood ratios have several advantages over sensitivity and specificity analysis because they are applied across a wider range of diagnostic scores, and generalize to settings with different base rates. We suggest that the diagnostic validity of many psychological tests may be underestimated by relying solely on traditional dichotomous sensitivity and specificity analysis.
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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 | 0.050 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".