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Record W2738361049 · doi:10.1089/bari.2017.0015

The Role of Quality of Life Instruments in Obesity Management: Review

2017· article· en· W2738361049 on OpenAlexaboutno aff
Roberto Accardi, Antonella Delle Fave, Silvia Ronchi, Stefano Terzoni, Emanuela Racaniello, Anne Destrebecq

Bibliographic record

VenueBariatric Surgical Practice and Patient Care · 2017
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineManagement of obesityObesityGerontologyInternal medicineWeight loss

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.326
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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