The risk for scurvy in children with neurodevelopmental disorders
Bibliographic record
Abstract
BACKGROUND: Scurvy, the disease resulting from vitamin C deficiency, is perceived as being rare and occurring predominantly in the past. However, scurvy continues to exist and may be encountered in children with medical/developmental conditions and/or restricted diet. Diagnosis can be challenging given the perceived rarity of the condition and nonspecific symptoms, including gingival disease. METHODS: We present a series of two cases of scurvy in which the affected children presented to medical attention with dental complaints. Additional cases of scurvy are described, based on the literature review of case reports/series published in the last 10 years. RESULTS: Literature review yielded 77 relevant case reports published in the English language since 2009. Most affected children had a previous diagnosis of a medical or developmental condition (especially autism spectrum disorder). Intraoral features (gingival swelling, pain, and bleeding) were noted in most of the identified cases of scurvy. Improvement in the oral features of scurvy occurred within days of vitamin C therapy initiation. CONCLUSIONS: Recognizing classic signs and symptoms of scurvy enables prompt diagnosis and avoids invasive investigations. Dentists may be in a unique position to facilitate prompt and accurate diagnosis of a condition that is relatively easy and safe to treat once identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".