SCAI Publications Committee Manual of Standard Operating Procedures: 2022 Update
Bibliographic record
Abstract
Evidence-based recommendations for clinical practice are intended to help health care providers and patients make decisions, minimize inappropriate practice variation, promote effective resource use, improve clinical outcomes, and direct future research. SCAI has been engaged in the creation and dissemination of clinical guidance documents since the 1990s. These documents are a cornerstone of the Society's education, advocacy, and quality improvement initiatives. The Publications Committee is charged with the oversight of SCAI's clinical documents program and has published the first iteration of this manual of standard operating procedures in 2019 to ensure consistency, methodological rigor, and transparency in the development and endorsement of the Society's documents. The manual has been updated based on feedback from the implementation of the original version to add specificity and expand the breadth of available document formats. The manual is intended for reference by the Publications Committee, document writing groups, external collaborators, SCAI representatives, peer reviewers, and anyone seeking information about the SCAI documents program.
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 | 0.118 | 0.389 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.090 | 0.150 |
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".