Challenges of Pelvic Imaging in Obese Women
Bibliographic record
Abstract
Obesity is a major global health concern affecting all ages, socioeconomic groups, and countries. Although men have higher rates of overweight, women have higher rates of obesity. In the United States, more than 60% of women are overweight or obese, with slightly more than one-third considered frankly obese. Obesity is a major risk factor for noncommunicable diseases such as diabetes mellitus, cardiovascular disease, hypertension, stroke, and specific cancers. Obesity is associated with increased mortality for all cancers, with the highest death rates occurring in the heaviest women. Obesity can contribute to missed diagnoses, nondiagnostic results of imaging studies, imaging examination cancellation because of weight or girth restrictions, scheduling of inappropriate examinations, and increased radiation dose exposure. The utility of the clinical examination is often limited in the obese woman, which results in an even greater reliance on imaging; however, the obese woman may experience a lowered quality of and less access to medical imaging. Recognition of equipment limitations, imaging artifacts, optimization techniques, and appropriateness of modality choices is critical to providing good patient care to this health-challenged group. The clinical indication, the patient's weight, and the body diameters are three key factors to consider when choosing the most appropriate examination. Familiarity with the optimization of imaging techniques across all modalities is important to convert potentially suboptimal examinations into diagnostic-quality studies. The aim of this review is to identify key areas in which obesity affects the imaging care of women with pelvic conditions and to outline strategies to address these areas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".