Outcomes of the first global multidisciplinary consensus meeting including persons living with obesity to standardize patient‐reported outcome measurement in obesity treatment research
Bibliographic record
Abstract
Quality of life is a key outcome that is not rigorously measured in obesity treatment research due to the lack of standardization of patient-reported outcomes (PROs) and PRO measures (PROMs). The S.Q.O.T. initiative was founded to Standardize Quality of life measurement in Obesity Treatment. A first face-to-face, international, multidisciplinary consensus meeting was conducted to identify the key PROs and preferred PROMs for obesity treatment research. It comprised of 35 people living with obesity (PLWO) and healthcare providers (HCPs). Formal presentations, nominal group techniques, and modified Delphi exercises were used to develop consensus-based recommendations. The following eight PROs were considered important: self-esteem, physical health/functioning, mental/psychological health, social health, eating, stigma, body image, and excess skin. Self-esteem was considered the most important PRO, particularly for PLWO, while physical health was perceived to be the most important among HCPs. For each PRO, one or more PROMs were selected, except for stigma. This consensus meeting was a first step toward standardizing PROs (what to measure) and PROMs (how to measure) in obesity treatment research. It provides an overview of the key PROs and a first selection of the PROMs that can be used to evaluate these PROs.
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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 | 0.133 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".