Quantifying patients' expectations prior to bariatric surgery with the <scp>BODY‐Q</scp> in an international multicentre cohort
Bibliographic record
Abstract
Many people living with obesity may have unrealistic expectations prior to undergoing bariatric surgery. These expectations can lead to frustration, regret and even worse psychological outcomes after surgery. Prior to undergoing bariatric surgery, patients completed the BODY-Q Expectations scale. This 15-item scale asks patients to imagine what their life will be like when 2 years have passed since bariatric surgery. Scores for expectations range from 0 (low) to 100 (high). Linear regression models were used to assess which of the patient characteristics could predict patients' expectations. From 2019 to 2020, a total of 333 patients were recruited from three different countries. The mean score on the BODY-Q Expectations scale was 73.1, ±20. Out of the 15 items on the Expectation scale, people living with obesity found it most likely that bariatric surgery would make them feel better and improve their energy, while it was reported least likely that they would look good without clothes on. Younger age (p = <.01) and race (non-white) (p = .046) were found to be significant predictors for higher scores on the Expectations scale. Participants who identified as non-white from the Netherlands and United States, and those aged under 40 years reported higher expectations compared with participants who identified as white race and were 40 years of age or older. Prior to bariatric surgery, patients aged under 40 years and those who identified as non-white for race had higher expectations that their life would change after bariatric surgery. These relevant findings should be considered when giving pre-operative counselling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".