On the Measurement of Change in Medical Research
Bibliographic record
Abstract
Measuring of change is essential in medical research. However, these measurements may have different goals and, traditionally, the ability to measure change has focused on sensitivity in a statistical sense, whereas little attention has been directed to the appropriate interpretation and analysis of change indicators. The present report examines some of the most important issues involved in measuring change with pre and post-test data when ordinal scales are used, and the conceptual problems pertaining to the use of these scales are also discussed. It can be said that there is still no agreement about the most adequate strategy for assessing health status change in a group of subjects, what caused the introduction of many indicators, most of which variations of the ES (Effect size: the mean of change scores divided by the standard deviation of the baseline scores) concept. The adequate interpretation of change scores in these cases demands a high degree of knowledge about what these changes mean to specific sub-groups of patients, as well as detailed information on their situation at baseline, such as score distributions. Researchers should strive for interpretations that take into account what "change" means for different patients.
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 | 0.415 | 0.531 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".