The effect of acute weight restoration on dietary fat preference in hospitalized patients with anorexia nervosa
Bibliographic record
Abstract
OBJECTIVE: Individuals with anorexia nervosa (AN) restrict fat intake. The Geiselman Food Preference Questionnaire-I© (FPQ) is a validated self-report instrument that yields a fat preference score (>100 = high fat preference, <100 = low fat preference). The goal of the study was to assess the utility of the FPQ in patients with AN. SPECIFIC AIMS: (1) to examine change in fat preference scores before and after weight restoration in hospitalized patients; and (2) to compare patients' scores before and after weight restoration to scores from healthy participants (HPs). METHOD: The FPQ was completed by 88 patients and 115 HPs. RESULTS: Compared with HPs, patients had significantly lower fat preference scores before (74.03 ± 32.03 vs. 102.93 ± 16.89, P < 0.001) and after (81.51 ± 26.89 vs. 102.92 ± 16.89, P < 0.001) weight restoration. Fat preference scores increased with weight gain (74.03 + 32.03 vs. 81.51 + 26.89, P < 0.01) but did not normalize in AN. DISCUSSION: Acutely weight restored patients continue to endorse decreased preference for high fat foods. The FPQ may be a useful metric by which to assess improvements in diet during treatment. Further study is warranted to validate the FPQ against observed food intake in AN. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:148-151).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".