The systematic use of evidence‐based methodologies and technologies enhances shared decision‐making in the 2018 International Consensus Conference on Patient Blood Management
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patient Blood Management (PBM) aims to optimize the care of patients who might need a blood transfusion. The International Consensus Conference on PBM (ICC-PBM) aimed to develop evidence-based recommendations on three topics: preoperative anaemia, red blood cell transfusion thresholds and implementation of PBM programmes. This paper reports how evidence-based methodologies and technologies were used to enhance shared decision-making in formulating recommendations during the ICC-PBM. MATERIALS & METHODS: Systematic reviews on 17 PICO (Population, Intervention, Comparison, Outcomes) questions were conducted by a Scientific Committee (22 international topic experts and one methodologist) according to GRADE (Grades of Recommendation, Assessment, Development and Evaluation) methodology. Evidence-based recommendations were formulated using Consensus Development Conference methodology. RESULTS: We screened 17 607 references and included 145 studies. The overall certainty in the evidence of effect estimates was generally low or very low. During the ICC, plenary sessions (100-200 stakeholders from a range of clinical disciplines and community representatives) were followed by closed sessions where multidisciplinary decision-making panels (>50 experts and patient organizations) formulated recommendations. Two chairs (content-expert and methodologist) moderated each session and two rapporteurs documented the discussions. The Evidence-to-Decision template (GRADEpro software) was used as the central basis in the process of formulating recommendations. CONCLUSION: This ICC-PBM resulted in 10 clinical and 12 research recommendations supported by an international stakeholder group of experts in blood transfusion. Systematic, rigorous and transparent evidence-based methodology in a formal consensus format should be the new standard to evaluate (cost-) effectiveness of medical treatments, such as blood transfusion.
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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 | 0.670 | 0.740 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.041 | 0.018 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".