Bibliographic record
Abstract
Collecting, analyzing, and interpreting data are essential components of biomedical research and require biostatistics. Doing various statistical tests has been made easy by sophisticated computer software. It is important for the investigator and the interpreting clinician to understand the basics of biostatistics for two reasons. The first is to choose the right statistical test for the computer to perform based on the nature of data derived from one's own research. The second is to understand if an analysis was performed appropriately during review and interpretation of others' research. This article reviews the choice of an appropriate parametric or nonparametric statistical test based on type of variable and distribution of data. Evaluation of diagnostic tests is covered with illustrations and tables.
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 | 0.269 | 0.351 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".