Risk Factors in Hospitalized Patients for Heparin-Induced Thrombocytopenia by Real World Database: A New Role for Primary Hypercoagulable States
Bibliographic record
Abstract
Background: The aims of the study were to identify predictors of heparin-induced thrombocytopenia (HIT) in hospitalized adults, and to find additional factors associated with higher odds of HIT in primary hypercoagulable states. Methods: A retrospective matched case-control study using discharge data from National Inpatient Sample database (2012 - 2014) was conducted. In primary outcome analysis, hospitalized patients with and without HIT were included as cases and controls, both matched for age and gender. In secondary outcome analysis, hospitalized patients with primary hypercoagulable states with and without HIT were included as cases and controls, both matched for age and gender. The statistical analyses were performed using Statistical Package for Social Sciences version 25. Results: There are several predictors of HIT in hospitalized patients, such as obesity, malignancy, diabetes, renal failure, major surgery, congestive heart failure, and autoimmune diseases. In patients with primary hypercoagulable states, the presence of renal failure (odds ratio (OR) 2.955, 95% confidence interval (CI) 1.994 - 4.380), major surgery (OR 1.735, 95% CI 1.275 - 2.361), congestive heart failure (OR 4.497, 95% CI 2.466 - 8.202), or autoimmune diseases (OR 1.712, 95% CI 1.120 - 2.618) further increases the odds of HIT. Conclusions: In hospitalized patients with primary hypercoagulable states, especially in association with renal failure, major surgery, congestive heart failure, or autoimmune diseases, unfractionated heparin should be used with caution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".