The Value of Bayes Theorem in the Interpretation of Subjective Diagnostic Findings: What Can We Learn from Agreement Studies?
Bibliographic record
Abstract
The Bayes theorem is advocated as the appropriate measure for the weight of evidence in medical decision making. It is based on the calculation of posttest probability as a function of the accuracy of the test and pretest probability. Nevertheless, for subjective diagnostic findings, there might be substantial variability in the accuracy among human observers, making the point estimate of posttest probability imprecise. Although there is limited evidence regarding the actual variability of accuracy among observers for the majority of diagnostic findings, classical observer agreement studies provide us with an indirect estimate of such variability. The aim of this work was to explicate the relationship between observer disagreement and variability of posttest probability. Using a random effects signal detection model with 3 stochastic components (between subject, between observer, and residual variations), the authors modeled diagnostic tests with various characteristics and calculated the expected between-observer disagreement and 95% interval of the observers' posttest probability. For the majority of simulated conditions, variation in posttest probability was surprisingly high, even in the presence of substantial agreement. Although the model is based on parametric assumptions, these results are a clue to a source of inaccuracy in the calculation of posttest probability. Practitioners should be aware of such variation in their clinical practice, and diagnostic studies need to develop strategies to address this uncertainty.
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 | 0.317 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.015 | 0.041 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".