A systematic assessment of the quality of reporting for platelet transfusion studies
Bibliographic record
Abstract
BACKGROUND: As evidence-based medicine assumes increasing importance, there is a need for high-quality reporting of clinical studies. A recent review of clinical platelet (PLT) studies indicated variability in reporting. We undertook a critical analysis of PLT transfusion studies to determine the quality of reporting. STUDY DESIGN AND METHODS: A systematic MEDLINE search for clinical studies of PLT transfusion was performed to identify articles. Relevant observational studies (OBS) were critiqued using the STROBE checklist and randomized controlled clinical trials (RCTs) using the CONSORT checklist. Studies were further evaluated with a PLT-specific checklist developed by the authors. Observations were analyzed descriptively and using Pareto analysis. RESULTS: A total of 772 articles were identified by the search. Eighty-six articles (23 RCTs and 63 OBS) met eligibility criteria. All RCTs, and a similar number of OBS (24), were randomly selected for analysis. Studies reported the scientific background and rationale, key results, and outcomes. OBS frequently did not consider bias and confounders. RCTs frequently did not explain bias, interim analyses, stopping rules, success of blinding, or weaknesses of multiple analyses. The PLT-specific critique found many studies adequately reported basics of the PLT product, PLT increment, and transfusion reactions. Studies frequently failed to report specific details of PLT compatibility, details of product preparation, and use of other blood products. CONCLUSION: Recently published articles of clinical PLT transfusion share common strengths and weaknesses. The quality of reporting may be improved by providing guidelines to authors and journal editors that list the essential elements of a well-reported clinical study of PLT 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 | 0.531 | 0.794 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.044 | 0.030 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".