Welfare for unaccompanied minors : A comparative study of Canada´s and Sweden´s implementation of the CRC
Bibliographic record
Abstract
In a population of patients experiencing thrombocytopenia while treated with heparin, bleeding and thromboses are well-appreciated complications, but their relative contributions to mortality have been less well described. In this population, the aims of this study were (1) to identify the independent predictors of bleeding and (2) to compare the incidence and the strength of association of bleeding and of new thromboses to in-hospital mortality. The independent predictors of bleeding and in-hospital mortality were identified using multivariate logistic regression models on the 1,478 patients who developed thrombocytopenia after their enrollment in the Complications After Thrombocytopenia Caused by Heparin (CATCH) study. The independent predictors of bleeding were chronic hematologic disorders, intra-aortic balloon pump, congestive heart failure, and platelet count nadir <120 x 10(9)/L. Although bleeding (n = 141 [10%]) and thromboembolic complications (n = 135 [9%]) were equally prevalent, the former was less strongly associated than the latter with in-hospital mortality (odds ratio 1.75, 95% confidence interval 1.01 to 3.03, and odds ratio 2.77, 95% confidence interval 1.67 to 4.61, respectively). In conclusion, medical management should be directed mainly at the prevention of thromboembolic complications, while additionally considering the risk for bleeding.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".