Predictive value of the 4Ts scoring system for heparin-induced thrombocytopenia: a systematic review and meta-analysis
Bibliographic record
Abstract
The 4Ts is a pretest clinical scoring system for heparin-induced thrombocytopenia (HIT). Although widely used in clinical practice, its predictive value for HIT in diverse settings and patient populations is unknown. We performed a systematic review and meta-analysis to estimate the predictive value of the 4Ts in patients with suspected HIT. We searched PubMed, Cochrane Database, and ISI Web of Science for studies that included patients with suspected HIT, who were evaluated by both the 4Ts and a reference standard against which the 4Ts could be compared. Quality of eligible studies was assessed by QUADAS-2 criteria. Thirteen studies, collectively involving 3068 patients, fulfilled eligibility criteria. A total of 1712 (55.8%) patients were classified by 4Ts score as having a low probability of HIT. The negative predictive value of a low probability 4Ts score was 0.998 (95% CI, 0.970-1.000) and remained high irrespective of the party responsible for scoring, the prevalence of HIT, or the composition of the study population. The positive predictive value of an intermediate and high probability 4Ts score was 0.14 (0.09-0.22) and 0.64 (0.40-0.82), respectively. A low probability 4Ts score appears to be a robust means of excluding HIT. Patients with intermediate and high probability scores require further evaluation.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".