Positive and negative predictive values from published studies can be misleading for decision-making in clinical practice: reply
Bibliographic record
Abstract
1Department of Internal Medicine, Innsbruck Medical University, Innsbruck and 2General Hospital of the Elizabethenians, Klagenfurt, Austria Sir, The letter to the editor by Rudwaleit and Sieper points out the important role of the likelihood ratio (LR) as a description of the diagnostic value of any diagnostic test in comparison to the restricted usefulness of positive and negative predictive values. The authors demonstrate in various examples the dependence of the positive and negative predictive values on the prevalence of the disease within the population tested. We totally agree with the conclusion of Rudwaleit and Sieper that highlighting the positive predictive value of specific antibodies cross-reacting with a 28 kDa Drosophila antigen in ankylosing spondylitis (AS) patients [1] may be misleading for clinical decision-making. We also share the opinion that testing patients suspicious for AS for the presence of antibodies cross-reacting with the 28 kDa Drosophila antigen may have an additional diagnostic value. The positive LRs, provided in Table 2, were calculated to be 1.9, 2.2 and 3.8 for the cut-off levels of ≥50, ≥60 and ≥75 U/ml, respectively. Choosing a higher cut-off of ≥125 U/ml results in a further increase in the positive LR (7.3, Du et al., unpublished) but decreases the sensitivity of this test below 20%. Then the question arises whether such a gain in the positive LR, paralleled with poor sensitivity, is helpful in clinical practice. Using a cut-off of ≥75 U/ml, the positive LR of 3.8 is comparable with the diagnostic value of inflammatory back pain, enthesitis of the heel or peripheral arthritis [2], with a sensitivity of 30.7%. [1] As serological signs of inflammation measured by the ESR and CRP levels are often absent in AS patients [3] and diagnosis of AS in HLA-B27 negative patients is unacceptably delayed, [4] a positive enzyme-linked immunosorbent assay (ELISA) test result can contribute to the diagnostic evaluation of these patients, expanding the diagnostic laboratory assessments available so far. Notably, in this study the presence of antibodies cross-reacting with the 28 kDa Drosophila antigen was independent of the HLA-B27 status in AS patients.
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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 | 0.017 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.033 | 0.039 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".