Iron Deficiency and Sleep/Wake Behaviors: A Scoping Review of Clinical Practice Guidelines—How to Overcome the Current Conundrum?
Bibliographic record
Abstract
Current evidence suggests that iron deficiency (ID) plays a key role in the pathogenesis of conditions presenting with restlessness such as attention deficit hyperactivity disorder (ADHD) and restless legs syndrome (RLS). In clinical practice, ID and iron supplementation are not routinely considered in the diagnostic work-up and/or as a treatment option in such conditions. Therefore, we conducted a scoping literature review of ID guidelines. Of the 58 guidelines included, only 9 included RLS, and 3 included ADHD. Ferritin was the most frequently cited biomarker, though cutoff values varied between guidelines and depending on additional factors such as age, sex, and comorbidities. Recommendations surrounding measurable iron biomarkers and cutoff values varied between guidelines; moreover, despite capturing the role of inflammation as a concept, most guidelines often did not include recommendations for how to assess this. This lack of harmonization on the interpretation of iron and inflammation biomarkers raises questions about the applicability of current guidelines in clinical practice. Further, the majority of ID guidelines in this review did not include the ID-associated disorders, ADHD and RLS. As ID can be associated with altered movement patterns, a novel consensus is needed for investigating and interpreting iron status in the context of different clinical phenotypes.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".