Comparison of risk‐scoring systems for heparin‐induced thrombocytopenia in cardiac surgery patients
Bibliographic record
Abstract
OBJECTIVES: Several risk-scoring tools have been developed to exclude heparin-induced thrombocytopenia (HIT) in patients with thrombocytopenia, but these scores have not been reproduced or compared in the cardiac surgery population. The objective of this study was to validate and compare the modified 4T's (m4T) and Lillo-Le Louet (LLL) scores for HIT screening in the cardiac surgery population. METHODS: In this nested case-control study, we retrospectively calculated the m4T and the cardiac surgery-specific score by LLL for 18 cases (HIT-positive) and 54 matched controls (HIT-negative) using characteristics known at the time the HIT assay was ordered post-cardiac surgery and compared their performances by their c-statistic (area under the receiver operating characteristic curve), sensitivity and specificity. RESULTS: The median time from surgery to HIT assay order was 9.5 days (IQR 3.75-11.0) in the HIT-positive group and 2 days (IQR 2.0-3.0) in the HIT-negative group (p < 0.0001). The c-statistics for the m4T and the LLL scores were 0.76 (95% CI 0.64-0.85) and 0.63 (95% CI 0.51-0.74), respectively (p = 0.051). Sensitivity and specificity were 61% and 91% for the m4T, and 94% and 32% for the LLL score. CONCLUSION: Performance of the m4T and LLL scores in discriminating HIT-positive from HIT-negative patients was modest among patients post-cardiac surgery. However, differences between the sensitivities of these scores suggest that the LLL score may be a safer tool for ruling out HIT in this population.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".