Quality of evidence‐based guidelines for platelet transfusion and use: A systematic review
Bibliographic record
Abstract
BACKGROUND: Guidelines for platelet (PLT) transfusion are an important source of information for clinicians. Although guidelines intend to increase consistency and quality of care, variation in methodology and recommendations may exist that could impact the value of a guideline. We aimed to determine the quality of existing PLT transfusion guidelines using the Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument and to describe the inconsistencies in recommendations. STUDY DESIGN AND METHODS: A systematic search was undertaken for evidence-based guidelines from January 1, 2013, to January 25, 2019. Citations were reviewed in duplicate for inclusion and descriptive data extracted. Four physicians appraised the guideline using the AGREE II instrument and the scaled score for each item evaluated was calculated. The protocol was registered in PROSPERO. RESULTS: Of 6744 citations, 6740 records were screened. Seven of 28 full-text studies met the inclusion criteria. The median scaled score (and the interquartile range of the scaled score) for the following items were as follows: scope and purpose, 94% (8%); stakeholder involvement, 63% (18%); rigor of development, 83% (14%); clarity of presentation, 94% (6%); applicability, 58% (20%); and editorial independence, 77% (4%). Overall quality ranged from 4 to 7 (7 is the maximum score). Inconsistent recommendations were on prophylactic PLT transfusion in hypoproliferative thrombocytopenia in the presence of risk factors and dose recommendations. CONCLUSION: Inconsistencies between guidelines and variable quality highlight areas for future guideline writers to address. Areas of specific attention include issues of stakeholder involvement and applicability.
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 | 0.066 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".