167PS Setting New Horizons In Optimizing Guideline Utility
Bibliographic record
Abstract
Background To maximise uptake, CPG recommendations must avoid of bias and be responsive to the needs of clinicians and patients from different populations and settings. Objectives/Goal To discuss three key challenges to CPG use: 1) building consensus and minimising conflicts of interest in formulating recommendations for specific patient populations; 2) taking account of patient multi-morbidity; 3) incorporating patient values and preferences for specific outcomes. Target group Suggested audience Guideline developers and writing groups, clinical researchers, users of guidelines (clinicians, patients). Moderator Prof Ian A Scott, Director of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia.Invited SpeakersDr Susan L Norris, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, USA. SLN is Technical Officer for the secretariat of the Guideline Review Committee at the World Health Association in Geneva, Switzerland and has conducted research on conflicts of interest. Professor Holger J Schünemann, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. HJS is co-chair of the GRADE working group, member of the GIN board of trustees and has co-authored reports on guideline methodology, including multimorbidity. Professor Gordon H Guyatt, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. GHG is co-chair of the GRADE working group and chaired the executive of 9th iteration of the American College of Chest Physicians Antithrombotic Guidelines.Description of session and speaker topicsSession will comprise 3 presentations (15 mins), one for each challenge, with 5 mins for questions of clarification then 30 mins of panel discussion.
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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 | 0.150 | 0.425 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".