Guidelines for reporting descriptive statistics in health research
Bibliographic record
Abstract
The quality of reporting of results of health studies has been the subject of several papers in the recent years. There are several guidelines published on the topic, but improvements have been very slow Lehana Thebane and Noori Akhtar-Danesh provide advice on how to report the analysis methods used to describe data and determine the descriptive statistics to use, and the accuracy with which to report the results.
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 | 0.341 | 0.719 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.039 | 0.056 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.056 | 0.035 |
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".