Predictors of clinical outcome in patients with heparin-induced thrombocytopenia treated with direct thrombin inhibition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We aimed to identify predictors of poor outcome in patients with heparin-induced thrombocytopenia, a serious immune-mediated reaction to heparin. All patients were treated with direct thrombin inhibition therapy, as part of two prospective studies. We performed a risk factor analysis of adverse outcomes (defined as death, amputation, new thrombosis, or their composite within a 37-day study period) in 809 patients from two reported prospective studies of the direct thrombin inhibitor argatroban in clinically diagnosed heparin-induced thrombocytopenia. We initially identified from among 14 baseline variables the significant predictors of poor outcome in the first study (304 patients), and then tested our resultant hypothesis in the second, independent study (505 patients), using multivariate analysis. Seven significant predictors were identified in the first study; three were confirmed in the second study. The strongest relationship occurred between the baseline platelet count and the composite of death, amputation, or new thrombosis (P = 0.0001), with the most severely thrombocytopenic patients being at greatest risk. The other significant associations were between renal impairment and death (odds ratio = 2.13, 95% confidence interval = 1.23-3.66, P = 0.007), and between cardiovascular surgery (particularly peripheral vascular surgery) and amputation (odds ratio = 3.39, 95% confidence interval = 1.65-6.95, P = 0.0009). In conclusion, in patients with clinically diagnosed heparin-induced thrombocytopenia, the severity of the baseline thrombocytopenia is the best predictor of death, amputation or thrombotic progression. The identification of higher risk subgroups for poor outcomes, such as patients with more severe thrombocytopenia or a history of renal impairment or peripheral vascular surgery, could allow more targeted therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it