When Can We Be Confident about Estimates of Treatment Effects
Bibliographic record
Abstract
Dr. Gordon Guyatt from the Department of Clinical Epidemiology and Biostatistics, McMaster University, moderated the topic When Can We Be Confident about Estimates of Treatment Effects? with Drs. Paul Glasziou from the Centre for Research in Evidence-Based Practice, Bond University, Victor Montori from the Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, and Holger Schunemann from the Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. The discussion focused primarily on: The concept of quality of evidence; traditional approaches to assessing quality of evidence; limitations of the hierarchy of evidence approach; and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Med Roundtable Gen Med Ed. 2014;1(3):178–184.
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 | 0.521 | 0.908 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.022 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".