Predictors of health‐related quality of life in 500 severely obese patients
Bibliographic record
Abstract
OBJECTIVE: To characterize health-related quality of life (HRQL) impairment in severely obese subjects, using several validated instruments. METHODS: A cross-sectional analysis of 500 severely obese subjects was completed. Short-Form (SF)-12 [Physical (PCS) and Mental (MCS) component summary scores], EuroQol (EQ)-5D [Index and Visual Analog Scale (VAS)], and Impact of Weight on Quality of Life (IWQOL)-Lite were administered. Multivariable linear regression models were performed to identify independent predictors of HRQL. RESULTS: Increasing BMI was associated with lower PCS (-1.33 points per 5 kg/m(2) heavier; P < 0.001), EQ-index (-0.02; P < 0.001), EQ-VAS (-1.71; P = 0.003), and IWQOL-Lite (-3.72; P = 0.002), but not MCS (P = 0.69). The strongest predictors (all P < 0.005) for impairment in each instrument were: fibromyalgia for PCS (-5.84 points), depression for MCS (-7.49 points), stroke for EQ-index (-0.17 points), less than full-time employment for EQ-VAS (-7.06 points), and coronary disease for IWQOL-Lite (-10.86 points). Chronic pain, depression, and sleep apnea were associated with reduced HRQL using all instruments. CONCLUSION: The clinical impact of BMI on physical and general HRQL was small, and mental health scores were not associated with BMI. Chronic pain, depression, and sleep apnea were consistently associated with lower HRQL.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".