‘Ferritin as an indicator of suspected iron deficiency in children with autism spectrum disorder: prevalence of low serum ferritin concentration’
Bibliographic record
Abstract
their BMI.Stress is also thought to affect menarche, but due to the highly subjective nature of this phenomenon, it is difficult to study.Given that our study included no clinical controls, our results could reflect stress associated with having a clinical condition.Females with cerebral palsy and females with severe cognitive impairment may also have delayed puberty.7,8 However, females with Down syndrome experience menarche at an earlier age than the general population.Age of menarche shows no deviation in fragile X syndrome.9 The factors producing this variation across conditions merit further study, but may include activity level (acting through BMI) as well as physiological factors specific to the conditions.Individuals with clinical conditions may have taken medications which alter their metabolic or endocrine status, thereby affecting timing of menarche.In conclusion we found a significant minority of women with autism have an extremely late onset of menarche.We have since been contacted by another woman with autism who, at the age of 26, has never experienced menarche.Even excluding these extreme cases, menarche was delayed in women with ASC compared with age-matched controls by 8 months.Whilst intriguing, our findings are limited in that they included only a single ethnic group and relied on participants recalling their age at menarche and self-reporting exclusionary clinical conditions; the study was also of a relatively small sample size.A comprehensive study of pubertal development, which takes account of nutritional status, medication, and other variables relevant to pubertal development in women with ASC, is needed before any strong conclusions can be drawn.Our results suggest that such a study would be worthwhile.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".