Clinical evaluation of patients living with obesity
Bibliographic record
Abstract
Obesity is a significant public health concern which is implicated in cardiometabolic disease, mechanical complications and psychiatric sequelae. BMI is currently used for diagnosis; however, it has limited sensitivity for adiposity in certain circumstances. This has led to the development of risk stratification tools like the Edmonton Staging criteria and the Kings Obesity Staging Criteria: these facilitate and guide comprehensive obesity-related complication assessment. Healthcare professionals working within obesity clinics should adopt evidence-based communication strategies, including shared decision-making, motivational interviewing, and realistic goal setting. It is also vital to avoid weight-stigmatising terminology in all aspects of care, as this can negatively impact patients. Primary care plays an essential part in obesity care and should work to promptly identify cases, initiate treatment and forward on to specialist services where appropriate. Clinical evaluation of the patient living with obesity should take a holistic approach and involve input from bariatric physicians, dietitians, psychologists, and bariatric surgeons, wider members of the multi-disciplinary team should be involved where needed. Clinicians should take a detailed history, examination and order laboratory tests to investigate for complications. Overall, with appropriate evaluation, these assessments can guide patient management and facilitate long-term improvement in health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".