Bibliographic record
Abstract
Whenever we create a test to screen for a disease, to detect an abnormality or to measure a physiological parameter such as blood pressure (BP), we must determine how valid that test is—does it measure what it sets out to measure accurately? There are lots of factors that combine to describe how valid a test is: sensitivity and specificity are two such factors. We often think of sensitivity and specificity as being ways to indicate the accuracy of the test or measure. In the clinical setting, screening is used to decide which patients are more likely to have a condition. There is often a ‘gold-standard’ screening test—one that is considered the best to use because it is the most accurate. The gold standard test, when compared with other options, is most likely to correctly identify people with the disease (it is specific), and correctly identify those who do not have the disease (it is sensitive). When a test has a sensitivity of 0.8 or 80% it can correctly identify 80% of people who have the disease, but it misses 20%. This smaller group of people have the disease, but the test failed to detect them—this is known as a false negative. A test that has an 80% specificity can correctly identify 80% of people in a group that do not have a disease, but it will misidentify 20% of people. That group of 20% will be identified as having the disease when they do not, this is known as a false positive. See box 1 for definitions of common terms used when describing sensitivity and specificity. Box 1 ### Common terms Sensitivity: the ability of a test to correctly identify patients with a disease. Specificity: the ability of a test to correctly identify people without the disease. True positive: the person has the disease and …
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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 | 0.093 | 0.381 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".