Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Resources for clinical research are limited. With increasing demand for patient-centred care, which is growing into an integral component of modern medicine, studying outcomes of patients with specific clinical characteristics is becoming increasingly important. Given the high cost of clinical trials and the time it takes to complete an investigation, it has become compulsory for investigators to assess not only treatment effects between the main randomized groups but also to try to identify clinically relevant subgroups that may particularly benefit from specific treatments. Publications of subgroup analyses turned out to be prevalent, and more importantly, these findings play a significant role in strategic planning and decision-making processes. Therefore, raising awareness among clinicians about the concepts and values of subgroup analysis is an aspect of improving patient outcomes. In this statistical primer, we give a broad introduction to the topic of subgroup analysis in scientific research. We furthermore discuss the concept of subgroup analysis; the motivation for assessing subgroups; the types of subgroup analyses and the paradigm of hypothesis-generating research; the proper statistical methods for the examination of subgroup effects; and the optimal approach for interpretation of results. Finally, this review establishes the comprehensive users' guide for analysing and reporting subgroup studies on a point-by-point basis, using real-world examples that may help readers to gain experience to pursue their own subgroup analyses or interpret those of others.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it