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Record W2902907490 · doi:10.1111/jebm.12330

Quality of clinical practice guidelines about red blood cell transfusion

2018· review· en· W2902907490 on OpenAlexfundno aff
Daniel Simancas‐Racines, Nadia Montero‐Oleas, Robin W.M. Vernooij, Ingrid Arévalo-Rodríguez, P. Calle Fuentes, Ignasi Gich, Ricardo Hidalgo, María José Martínez‐Zapata, Xavier Bonfill, Pablo Alonso‐Coello

Bibliographic record

VenueJournal of Evidence-Based Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIICanadian Medical AssociationNational Institute for Health and Care ExcellenceAgency for Healthcare Research and QualityUniversitat Autònoma de BarcelonaWorld Health Organization
KeywordsMedicineGuidelineScope (computer science)CLARITYIntensive care medicineComputer sciencePathology

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearch
Domain: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.248
metaresearch head score (Gemma)0.556
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score0.927

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2480.556
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0180.015
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0080.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.585
GPT teacher head0.578
Teacher spread0.007 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Study designObservational
DomainEvaluation
GenreReview

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".

Quick stats

Citations30
Published2018
Admission routes1
Has abstractyes

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