A Core set of patient‐reported outcome measures to measure quality of life in obesity treatment research
Bibliographic record
Abstract
The lack of standardization in patient-reported outcome measures (PROMs) has made measurement and comparison of quality of life (QoL) outcomes in research focused on obesity treatment challenging. This study reports on the results of the second and third global multidisciplinary Standardizing Quality of life measures in Obesity Treatment (S.Q.O.T.) consensus meetings, where a core set of PROMs to measure nine previously selected patient-reported outcomes (PROs) in obesity treatment research was established. The S.Q.O.T. II online and S.Q.O.T. III face-to-face hybrid consensus meetings were held in October 2021 and May 2022. The meetings were led by an independent moderator specializing in PRO measurement. Nominal group techniques, Delphi exercises, and anonymous voting were used to select the most suitable PROMs by consensus. The meetings were attended by 28 and 27 participants, respectively, including a geographically diverse selection of people living with obesity (PLWO) and experts from various disciplines. Out of 24 PROs and 16 PROMs identified in the first S.Q.O.T. consensus meeting, the following nine PROs and three PROMs were selected via consensus: BODY-Q (physical function, physical symptoms, psychological function, social function, eating behavior, and body image), IWQOL-Lite (self-esteem), and QOLOS (excess skin). No PROM was selected to measure stigma as existing PROMs deemed to be inadequate. A core set of PROMs to measure QoL in research focused on obesity treatment has been selected incorporating patients' and experts' opinions. This core set should serve as a minimum to use in obesity research studies and can be combined with clinical parameters.
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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 | 0.087 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".