Diagnostic and Therapeutic Decision-Making: Exploring the Role of Pretest Probability in Patients with Rotator Cuff Pathology
Bibliographic record
Abstract
Purpose: The purpose of this article is to describe the indicators of diagnostic validity and to demonstrate the informative value of the pretest probability of two tests, the supraspinatus test and magnetic resonance imaging (MRI), on diagnosis and management by using three case scenarios of rotator cuff pathology as examples. Summary of Key Points: Patients' attributes and validity indices of clinical tests are important to consider in reaching an optimal diagnosis. We compared the impact of the supraspinatus (Jobe) test with MRI of the shoulder in three fictional cases with different severity of rotator cuff pathology. The methodological approach of clinical decision-making at the patient level is discussed. Conclusion and Recommendations: Clinicians are required to make decisions based on incomplete data and imperfect clinical tests with falsenegative and false-positive results. Estimating the pretest probability of pathology is often an educated guess. However, without an explicit estimate of the pretest probability of suspected pathology, a test result has no clearly defined informative value. The Bayesian approach provides a logically correct way to arrive at a specific post-test probability.
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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 | 0.037 | 0.327 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".