What an Internist Needs to Know about Statistics
Bibliographic record
Abstract
The basics of medical statistics can be readily understood but are often approached by clinicians as if mysterious or forbidding. This may be because statistics was poorly taught at medical school or due to a tendency for articles on statistics to rapidly overcomplicate concepts. It is our hope that this series will be enlightening and provide a solid grounding in the building blocks to all evidence-based medicine. This series is divided into the three main statistical areas: descriptive statistics as may be used commonly in audit projects, inferential statistics as may be used in therapeutic trials, and diagnostic tests in which sensitivity and specificity are important. Statistical concepts are illustrated with examples predominantly from the critical care literature. The choice of examples, however, should be regarded as non-significant when compared with any personal clinical practice. (There is no p value for this statement.)
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 | 0.042 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.018 | 0.021 |
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".