Trends in Anorexia Nervosa Research: An Analysis of the Top 100 Most Cited Works
Bibliographic record
Abstract
Analysis of highly cited papers provides unique insights into the status of research in a given field. We sought to identify the top 100 most highly cited papers in the field of anorexia nervosa (AN). A free, publically accessible software was used to conduct an online search of publications with accompanying citation data. Search terms were selected to focus on papers dealing predominantly with AN, and the results manually screened to exclude out-of-scope publications. Papers in bulimia nervosa, eating disorder not otherwise specified and binge-eating disorder, were not included. The top 100 most highly cited papers in the AN field were identified. Of these, 34 garnered greater than 400 citations, classifying them as 'citation classics'. These works were divided into five categories, those dealing with epidemiological trends, medical/psychiatric comorbidities, treatment, mechanisms of disease and measurement/classification. Publications examining the epidemiology and underlying mechanisms of AN account for the majority of the top 100 papers. Scales and measurement tools have had the greatest impact, garnering the greatest number of average citations per paper. Although reasonably diverse, the top 100 papers highlight areas still lagging behind, including the neuroscience of AN as well as research into novel treatment strategies.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.090 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".