Quality of clinical practice guidelines about red blood cell transfusion
Bibliographic record
Abstract
BACKGROUND: Red blood cell (RBC) transfusions are essential in health care. The quality of recommendations included in clinical practice guidelines (CPG), regarding this intervention, has not been systematically evaluated. This paper systematically assessed CPGs for RBC-transfusion, to appraise their methodological quality, to explore changes in quality over time, and to assess the consistency of the hemoglobin threshold (HT) recommendations. METHODS: We searched for CPGs that included recommendations of RBC-transfusion in generic databases, compiler entities, registries, clearinghouses and guideline developers. Three reviewers extracted data on CPGs characteristics and HT recommendations, independently appraised the quality of the studies using AGREE II and resolved disagreements by consensus. RESULTS: We examined 16 CPGs. Mean scores (mean ± SD) were: scope and purpose (59.4% ± 19.8%), stakeholder involvement (43.2% ± 22.6%), rigor of development (50% ± 25%), clarity of presentation (74.4% ± 12.6%), applicability (19.4% ± 18.8%), and editorial independence (41% ± 30%). Seven CPGs recommended a restrictive strategy for RBC transfusion; four CPGs gave a guarded statement considering an HT of 7 g/dL, as safe to prescribe an RBC transfusion. Eight CPGs did not provide an HT stating that RBC transfusions should not be prescribed by HT alone. CONCLUSIONS: Only 3 out of the 16 evaluated CPGs were "recommended" by the independent evaluators. Four domains "stakeholder involvement," "rigor of development," applicability," and "editorial independence" had serious shortcomings. Recommendations about the use of an HT for RBC-transfusion were heterogeneous among guidelines. Greater efforts are needed to provide high-quality CPGs in the RBC-transfusion practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.248 | 0.556 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".