The Role of Quality of Life Instruments in Obesity Management: Review
Bibliographic record
Abstract
Background and Scope: Obesity represents a public health concern worldwide; it is associated with a high mortality risk and impairment in quality of life (QoL). Relationship between weight loss and QoL improvements was highlighted by several studies. This article aims to summarize the literature investigating QoL among obese persons. Attention will be paid to studies assessing QoL among obese patients undergoing bariatric surgery before and after surgical treatment and to the related measurement instruments. Methods: A literature review was conducted on the major biomedical databases. Results: Compared with general population, persons with obesity report lower QoL levels in most life domains. The global QoL improvement reported in all domains after bariatric surgery can be related to weight loss and its long-term stability. Although several tools were developed to assess QoL in obese persons, they are not suited to capture the needs of people with obesity. The promising results obtained through the Laval Questionnaire suggest the importance of expanding this research domain, to identify the best assessment tools for use in clinical practice. Conclusion: The longitudinal assessment of the different QoL components can be useful to monitor the changes induced by the treatment over time.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".