Morbidity and health-related quality of life of patients accessing laparoscopic sleeve gastrectomy: a single-centre cross-sectional study in one province of Canada
Bibliographic record
Abstract
In Canada, severe obesity (BMI ≥ 35 kg/m 2 ) affects 5% or 1.2 million adults. Bariatric surgery is the only effective treatment for severe obesity, but the demand for publicly funded procedures is high and capacity limited. Little is known in Canada about the types of patients undergoing these procedures, especially laparoscopic sleeve gastrectomy (LSG). The study objective is to examine the socio-demographic profile, morbidity and HRQoL of patients accessing LSG in one Canadian province. Health status and HRQoL were examined in patients ( n = 195) undergoing LSG. HRQoL was assessed using the EQ-5D-3L, SF-12v2 and the Impact of Weight on Quality of Life-lite questionnaire. Mean age and BMI were 44 and 49 kg/m 2 and most were women (82%). Pre-surgery, comorbidities were sleep apnea (65%), dyslipidemia (48%), hypertension (47%) and osteoarthritis (44%). Patients reported impaired HRQoL with 44–67% reporting problems in mobility, usual activities, pain and anxiety/depression. Physical health was impaired more than mental health. There were few socio-demographic differences between women and men, but significant differences in comorbid conditions such as sleep apnea, dyslipidemia, hypertension and gout exist ( p < .05). Women reported fewer problems with self-care (9.5% vs. 25.0%, p < .05), and better overall health (VAS 61.5 vs. 52.0, p < .05) and General Health (39.3 vs. 32.9, p < .05), but greater impairment in self-esteem (27.3 vs. 44.1, p < .01) and sexual life (49.2 vs. 63.6, p < .05). Before LSG, patients reported significant morbidity and impaired HRQoL. Although baseline characteristics were similar between men and women, gender specific differences were observed in comorbid profile and HRQoL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".