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Record W4387357249 · doi:10.1080/20469047.2023.2262787

Scurvy: old disease, new lessons

2023· review· en· W4387357249 on OpenAlexafffund
Laura M. Kinlin, Michael Weinstein

Bibliographic record

VenuePaediatrics and International Child Health · 2023
Typereview
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersInstitute of Human Development, Child and Youth HealthCanadian Institutes of Health Research
KeywordsMedicineScurvyDiseasePediatricsIntensive care medicineGerontologyInternal medicineVitamin C

Abstract

fetched live from OpenAlex

Scurvy, the condition associated with severe vitamin C deficiency, is believed to be one of the oldest diseases in human history. It was particularly prevalent during the Age of Sail, when long sea voyages without access to fresh food resulted in an epidemic which claimed millions of lives; however, scurvy has existed across time and geography, occurring whenever and wherever diets are devoid of vitamin C. Young children, specifically, were affected by the emergence of ‘infantile scurvy’ in the 19th century owing to the use of heated milk and manufactured infant foods of poor nutritional quality. Scurvy continues to occur in at-risk groups. In children and youths, it is primarily observed in the context of autism spectrum disorder and feeding problems such as a limited food repertoire and high-frequency single food intake. Diagnosis may be delayed and invasive testing undertaken owing to clinicians’ lack of familiarity with the disease, or the mistaken assumption that it is exclusively a disease of the past. The aetiology, clinical manifestations and treatment of scurvy are described. Its long history and current epidemiology are also reviewed, demonstrating that scurvy is very much a disease of the present. It is suggested that future efforts should focus on (i) anticipatory guidance and early nutritional intervention, informed by an understanding of scurvy’s epidemiology, with the aim of preventing the disease in those at risk, and (ii) prompt recognition and treatment to minimise morbidity and healthcare costs.Abbreviations: ASD: autism spectrum disorder.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.145
GPT teacher head0.453
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations19
Published2023
Admission routes2
Has abstractyes

Explore more

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