Bibliographic record
Abstract
Scurvy is a nutritional deficiency which has currently become uncommon. Accurate history taking, strong suspicion and good clinical correlation especially in high risk groups are important in making an early diagnosis. A 9-year-old boy with underlying autism was presented to the Pediatric Dental Clinic with gingival swelling, fever and refusal to walk. The child was subsequently referred to the Pediatric Medical team for suspicion of a hematological malignancy. After a thorough assessment, he was diagnosed of having scurvy due to his peculiar diet habit. Vitamin C supplementation was started as diagnostic and therapeutic measures. Scurvy is a disease caused by chronic vitamin C deficiency. Though it has become uncommon, it still exists in high risk populations. The clinical manifestation of scurvy could be variable and non-specific. In practice, the diagnosis of scurvy is based on history and clinical findings. In cases of suspicion, resolution of disease manifestations after vitamin C supplementation remains the best diagnostic evidence. High index of suspicion is crucial to diagnose scurvy early in modern era. Early diagnosis is important for prompt initiation of treatment and fast resolution of symptoms. Int J Clin Pediatr. 2018;7(4):59-62 doi: https://doi.org/10.14740/ijcp321
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".