A systematic review of COVID-19 and the presentation of avoidant/restrictive food intake disorder and avoidant/restrictive food intake disorder-like symptoms
Bibliographic record
Abstract
BACKGROUND: The adverse effects of COVID-19 and the associated restrictions on eating disorder populations have been discussed in recent literature. However, little is known about the presentation of cases with avoidant/restrictive food intake disorder (ARFID) during this period. AIMS: To explore the extent of the literature on the presentation of ARFID, and ARFID-like cases, during the COVID-19 pandemic. METHOD: Cochrane Library, CINAHL (EBSCO), PsycINFO (EBSCO), EMBASE (Ovid) and Medline (Ovid) were searched for publications between March 2020 and May 2023. Google Scholar and reference lists were hand searched. At least two reviewers independently screened each paper. Narrative synthesis was used. RESULTS: = 4) quality. Findings did not suggest an increase in ARFID cases during the COVID-19 pandemic, although it is unclear if this is because of a lack of impact or underrecognition of ARFID. A need for a multidisciplinary approach to differentiate between ARFID and organic causes of ARFID-like presentations (e.g. gastrointestinal effects of COVID-19) was highlighted. CONCLUSIONS: Publications specifically pertaining to ARFID presentations during the COVID-19 pandemic have been few. Papers found have been of small sample sizes and lack subanalyses for ARFID within broader eating disorder samples. Continued surveillance is needed to evaluate any COVID-19-specific effects on the development, identification, treatment and outcomes of ARFID.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".