Feminist issues in clinic care, research, and healthcare professionals in thrombosis and hemostasis
Bibliographic record
Abstract
Feminist issues in clinic care, research, and healthcare professionals in thrombosis and hemostasis Gender biases and inequities contribute to poorer clinical outcomes for female patients, and negatively affect their female caregivers.The McKinsey Health Institute recently estimated the economic burden and opportunity cost to be US $1 trillion for not closing this women's health gap. 1 Structural gender biases undermine current clinical care and manifest in a multitude of ways, 2 in the setting of thrombosis and hemostasis we have examples of dismissing clinical symptoms to gaps in understanding gender-specific presentations of common diseases in women, such as acute myocardial infarction, or uniquely female conditions like hypermenorrhea and associated iron-deficiency anemia.3 Stereotyping and underrepresentation of female providers further compound the problem as they can erode patient trust which has been linked to care avoidance.4 These systemic challenges collectively can contribute to delayed diagnoses, inadequate symptom relief, suboptimal treatment, and, ultimately, poorer clinical outcomes.Clinical research suffers as much as any area from gender bias.In a world where most research funding is used to address diseases of men, and thus the majority of recruited patients are men, women's issues have been underfunded for decades.5 The most
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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 | 0.075 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.040 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".