Evidence-based Medicine: Answering Questions of Diagnosis
Bibliographic record
Abstract
Using medical evidence to effectively guide medical practice is an important skill for all physicians to learn. The purpose of this article is to understand how to ask and evaluate questions of diagnosis, and then apply this knowledge to the new diagnostic test of CT colonography to demonstrate its applicability. Sackett and colleagues have developed a step-wise approach to answering questions of diagnosis: Step1: Define a clinical question and its four components: Patient, intervention, comparison and outcome. Step 2: Find the evidence that will help answer the question. PubMed Clinical Queries is an efficient database to accomplish this step. Step 3: Assess whether this evidence is valid and important. A quick review of the methods and results section will help to answer these two questions. Step 4: Apply the evidence to the patient. This step includes: assessing whether the test can be used; determining if it will help the patient; finding whether the study patients are similar to the patient in question; determining a pretest probability; and deciding if the test will change one's management of the patient. A relatively new diagnostic test, CT colonography, is explored as a scenario in which the steps presented by Sackett et al.1 can be helpful. A patient who is interested in completing a CT colonography instead of a colonoscopy is the basis of the discussion. Because a CT colonography does not detect polyps of less than 10 mm accurately, many patient are not likely to prefer this test over a colonoscopy. Evidence-based medicine is an effective strategy for finding, evaluating, and critically appraising diagnostic tests, treatment and application. This skill will help physicians interpret and explain the medical information patients read or hear about.
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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 | 0.235 | 0.440 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".