Errors in patient perception of caloric deficit required for weight loss—observations from the Diet Plate Trial <sup>*</sup>
Bibliographic record
Abstract
Persons with obesity may be poor estimators of caloric content of food. Health care professionals encourage patients to consult nutritional labels as one strategy to assess and restrict caloric intake. Among subjects enrolled in a weight loss clinical trial, the objective is to determine the accuracy of subjects' estimates of caloric deficit needed to achieve the desired weight loss. A 6-month controlled trial demonstrated efficacy of a portion control tool to induce weight loss in 130 obese people with type 2 diabetes. All subjects had previously received dietary teaching from a dietician and a nurse. At baseline, patients were asked how much weight they would like to lose and to quantitatively estimate the caloric deficit required to achieve this weight loss. The stated amount of weight loss desired ranged from 4.5 to 73 kg, with an average of 26.6 kg (n = 127 respondents). Only 30% of participants were willing to estimate the required caloric deficit to lose their target weight. Subjects' per kilograms estimate of caloric deficit required ranged from 0.7 to 2,000,000 calories/kg with a median of 86 calories/kg. Nearly half of subjects (47.4%) underestimated the total required caloric deficit to achieve their target weight loss by greater than 100,000 calories. Despite attendance at a diabetes education centre, this population of obese individuals had a poor understanding of the quantitative relationship between caloric deficit and weight loss. Educational initiatives focused upon quantitative caloric intake and its impact on weight change may be needed to assist obese patients in setting appropriate weight loss goals and achieving the appropriate daily caloric restriction required for success.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".