What is the Best Way to Produce Consensus and Buy in to Guidelines for Rectal Cancer?
Bibliographic record
Abstract
Evidence-based guidelines are important tools and common pathways for translating evidence into clinical practice. It is most urgently needed when significant heterogeneity in practice exist. Actively engaging opinion leaders in the process of evidence-based guidelines development is important for several reasons. These include allowing the collective views of the practice communities to be represented, resolving heterogeneity in practice through discussion, and allowing credible recommendations to be formulated. Most importantly, the process itself is a tool for facilitating dissemination and implementation. Recognizing the gap between practice pattern and guideline recommendations, and devising strategies to address it represent an important step toward maximizing concordance between guideline and practice. Evidence-based recommendations serve as important reference points, against which we can measure, debate, and innovate from.
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 | 0.175 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".