A multicentred study to validate a consensus bleeding assessment tool developed by the biomedical excellence for safer transfusion collaborative for use in patients with haematological malignancy
Bibliographic record
Abstract
BACKGROUND: There continues to be uncertainty about the optimal approach to documenting bleeding data in platelet transfusion trials, with a desire to apply a common assessment tool across all trials. With this in mind, a consensus bleeding assessment tool (BAT) has been developed by the Biomedical Excellence for Safer Transfusion (BEST) collaborative, based on review of data collection forms used in published randomized trials and following content validation with a range of healthcare professionals at seven haematology centres through BEST members. This study aimed to evaluate reliability and reproducibility of the consensus BAT. METHODS: Replicated clinical assessments of bleeding were undertaken by participants with haematological malignancies recruited at four haematology centres in an international, multicentred, observational study. Concordance of repeat assessments was calculated for agreement in site and grade of bleeding observed. RESULTS: Forty patients consented to participate, and 13 trained bleeding assessors collected these data. Bleeding assessments were carried out on 113 separate days. Of all 225 bleeding assessments, 204 were compared for grade concordance, and 160 were compared for site concordance. There was very good grade concordance (83%, 95% confidence interval 74-93%) and good bleeding site concordance (69%, 95% confidence interval 57-79%) in observations of bleeding. Discordance was primarily in relation to assessing skin bleeding. CONCLUSIONS: Alongside a structured training programme, levels of concordance for a consensus BAT were high. Researchers using assessment tools for bleeding need to balance comprehensive data collection against potential loss of accuracy for some types of bleeding, such as skin findings.
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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 | 0.081 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".