Prevalence of Comorbidities and Baseline Characteristics of LAP-BAND AP® Subjects in the Helping Evaluate Reduction in Obesity (HERO) Study
Bibliographic record
Abstract
OBJECTIVE: To describe the baseline characteristics in patients who chose placement of a LAP-BAND AP® System (LBAP) and participated in the Helping Evaluate Reduction in Obesity (HERO) Study across regions. PATIENTS AND METHODS: HERO is a five-year, prospective, multicenter, international study of patients with LBAP placement between July 22, 2009 and January 31, 2011. In addition to baseline and peri-surgery clinical data, seven follow up visits are scheduled at 3, 6 and 12 months, and annually through year five. Data collection included family and medical history, clinical outcomes, laboratory data, health-related quality of life (HRQoL), productivity, healthcare resource utilization, and adverse events. RESULTS: LBAP were placed in 1106 enrolled patients; 56.6% from the US, 26.3% from Europe, 7.1% from Canada, and 10.0% from Australia. The majority were female (n = 877 (79.3%)) with a mean age of 43 years (s.d. = 11.4) and mean body mass index of 45.1 kg/m(2) (s.d. = 6.9). The most common comorbidities were hypertension (HTN) (overall = 42.9%) and diabetes (overall 22.2%, with 27% from the US and 14% from Europe). Overall, less than 5% had a history of cardiovascular disease. The prevalence rates of HTN, diabetes and cardiovascular disease were significantly (p<0.001) higher in men than in women across all regions. Overall HRQoL also worsened with increasing BMI. CONCLUSIONS: The HERO study is the first large, multinational and long-term registry with the LBAP. This study will provide real-world outcomes data on LAGB that will help inform patient choice, clinician treatment strategies, and payer reimbursement decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".