Viewpoint: Diagnosis in primary care: probabilistic reasoning
Bibliographic record
Abstract
This article develops the concept of probabilistic reasoning as one of the techniques clinicians use in making a diagnosis. We develop the concept that every question and every examination is a diagnostic test ultimately leading to a rule in or rule out of a diagnosis. We also develop the concept of pre-test probability pointing out that false positive tests are an issue in low-prevalence settings and false negative tests are a problem. Investigative tests work best in medium-prevalence settings. The purpose of taking a history and conducting an examination is to increase the pre-test probability to a point where either treatment is commenced or more expensive/time-consuming/dangerous tests are indicated. Pre-test probabilities on their own can be used to rule out conditions. We also show how pre-test probabilities relate to the Fagan nomogram which enables visualisation of large changes in post-test probabilities which can lead to treatment/further investigation. KEYWORDS: Likelihood ratio; pre-test and post-test probability; diagnostic accuracy; probabilistic reasoning
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".