Gender: does it have a role in bleeding time in Caucasians with well controlled type 2 diabetes?
Bibliographic record
Abstract
Background: Bleeding times are decreased in type 2 diabetics presenting an enhanced risk of myocardial infarction and subsequent death. It is controversial whether males have a greater risk of myocardial infarction and resultant death in type 2 diabetes. Objective: The purpose of this study was to review the literature regarding gender in bleeding time and to test the hypothesis that there would be gender inequality in bleeding time in well-controlled Caucasian Type 2 diabetics in Cape Breton, Nova Scotia. This study revealed significantly shorter bleeding times in males. Thus it may be that males should be more aggressively treated to increase bleeding time and hence to more equitably manage the risk of myocardial infarction and subsequent death. Ultimately it will have to be determined what bleeding time thresholds are suitable for intervention and indeed what the most appropriate intervention is at each threshold and what role gender may play in these features in type 2 diabetics. However, this was only a very small study and a much larger one would answer whether there is gender inequality in bleeding time among persons with well-controlled type 2 diabetes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".