Index
Bibliographic record
Abstract
CHEERS.See Consolidated Health Economic Evaluation Reporting Standards (CHEERS) Clinical Data Interchange Standards Consortium (CDISC) Protocol Representation Group, 58-59 CLIP (Clinical and Laboratory Images in Publications), 286-294 components of documentation acquire the image, 288, 293-295 image, 287-288 image meaning, 291 image selection, 288-289 indicate the image, 290 modifications of image, 289 development process, 292 placement of information in text, 291 Cluster randomized trials, CONSORT for, 122-132 checklist, 125t -129t creators' preferred bits, 131 endorsement and adherence, 130 evidence of effectiveness of guideline, 130 extensions and implementations, 124 flow diagram, 132f future plans, 132 history/development, 123 intraclass correlation coefficient (ICC), 124 mistakes and misconceptions, 131 related activities, 128 use of guideline, 124, 128 version, current vs. previous, 124 Cochrane Collaboration's Qualitative Research Methods Group, 222 Conduct excellent, 43, 44 inadequate, 44 Consolidated Health Economic Evaluation Reporting Standards (CHEERS), 304 CONSORT (Consolidated Standards of Reporting Trials), 80-90 Guidelines for Reporting Health Research: A User's Manual, First Edition.
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 | 0.020 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.781 | 0.450 |
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".