Obsessed with Healthy Eating: A Systematic Review of Observational Studies Assessing Orthorexia Nervosa in Patients with Diabetes Mellitus
Bibliographic record
Abstract
Orthorexia nervosa (ON) is an unspecified feeding or eating disorder (USFED) characterized by an exaggerated, unhealthy obsession with healthy eating. Τypical eating disorders (EDs) and USFEDs are common among patients with diabetes mellitus (DM), which complicates metabolic control and disease outcomes. The present systematic review summarizes the evidence on the prevalence of ON symptomatology among patients with DM. PubMed, Web of Science, Scopus, and grey literature were searched, and relevant observational studies were screened using the Rayyan software. The quality of the studies was assessed using the appraisal tool for cross-sectional studies (AXIS) and the Newcastle-Ottawa scale (NOS). Out of 4642 studies, 6 fulfilled the predefined criteria and were included in the qualitative synthesis. Most studies relied on the ORTO-15 or its adaptations to identify ON among patients with DM. No apparent sex or age differences exist regarding the prevalence of ON symptoms. None of the studies compared the prevalence of ON in patients with type 1 and type 2 DM. Most of the research was of average to good methodological quality. In conclusion, patients with DM often exhibit ON tendencies, although research is still limited regarding the etiology or mechanistic drivers behind ON and the characteristics of patients with a dual ON-DM diagnosis.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".