A new tool to assess bleeding severity in patients with chemotherapy‐induced thrombocytopenia (CME)
Bibliographic record
Abstract
BACKGROUND: Current scales to measure bleeding in clinical trials are inadequate. The aim of this study was to develop a simple, valid, and reliable measurement tool to categorize the severity of bleeding in patients with chemotherapy-induced thrombocytopenia (CIT). STUDY DESIGN AND METHODS: Measurement theory was used to develop the Bleeding Severity Measurement Scale (BSMS) in four steps: 1) identification of the patient population, 2) item generation and reduction, 3) reviewing the items and formatting the scale, and 4) evaluation of psychometric properties. Feasibility was tested in a pilot study. Content and face validity were assessed by expert review. Psychometric evaluation included determination of intra- and interrater reliability and construct and criterion validity. RESULTS: The final BSMS defined two grades of bleeding: not clinically significant (Grade 1) and clinically significant (Grade 2). Grade 2 bleeds were defined as bleeds resulting in morbidity, requiring interventions, or directly causing death. The BSMS had excellent interrater (intraclass correlation coefficient [ICC], 0.80) and intrarater (ICC, 1.0) reliability and good construct and criterion validity. The BSMS distinguished between patients with different bleeding severities. CONCLUSION: Using rigorous methods, we designed a simple bleeding assessment tool with excellent psychometric properties for patients with CIT. Use of this scale in clinical trials should provide valid and reliable assessments of bleeding.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".