Blindness Caused by a Junk Food Diet
Bibliographic record
Abstract
Letters21 April 2020Blindness Caused by a Junk Food DietAlison M.R. Castle, BSc, MD, Pranesh Chakraborty, MD, and Michael Geraghty, MB, MScAlison M.R. Castle, BSc, MDChildren's Hospital of Eastern Ontario, Ottawa, Ontario, Canada (A.M.C., P.C., M.G.)Search for more papers by this author, Pranesh Chakraborty, MDChildren's Hospital of Eastern Ontario, Ottawa, Ontario, Canada (A.M.C., P.C., M.G.)Search for more papers by this author, and Michael Geraghty, MB, MScChildren's Hospital of Eastern Ontario, Ottawa, Ontario, Canada (A.M.C., P.C., M.G.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-0015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Harrison and colleagues' case report (1) first came to our attention because of the extensive media coverage it received. We appreciate the public discussion on healthy dietary choices and nutritional deficiencies that it has incited. However, we caution the authors about their diagnosis of nutritional optic neuropathy. They base their diagnosis on the patient's elevated levels of homocysteine (Hcy) and methylmalonic acid (MMA) in the context of a history of vitamin B12 (cobalamin) levels in the lower range of normal. Although metabolism of both Hcy and MMA is vitamin B12 dependent and elevations of these levels can ...References1. Harrison R, Warburton V, Lux A, et al. Blindness caused by a junk food diet. Ann Intern Med. 2019;171:859-61. [PMID: 31476767]. doi:10.7326/L19-0361 LinkGoogle Scholar2. Stabler SP. Clinical practice. Vitamin B12 deficiency. N Engl J Med. 2013;368:149-60. [PMID: 23301732] CrossrefMedlineGoogle Scholar3. Sloan JL, Carrillo N, Adams D, et al. Disorders of intracellular cobalamin metabolism. In: Adam MP, Ardinger HH, Pagon RA, et al, eds. GeneReviews. Seattle: University of Washington, Seattle; 1993. Google Scholar4. Trakadis YJ, Alfares A, Bodamer OA, et al. Utiate on transcobalamin deficiency: clinical presentation, treatment and outcome. J Inherit Metab Dis. 2014;37:461-73. [PMID: 24305960] CrossrefMedlineGoogle Scholar5. Huemer M, Scholl-Bürgi S, Hadaya K, et al. Three new cases of late-onset cblC defect and review of the literature illustrating when to consider inborn errors of metabolism beyond infancy. Orphanet J Rare Dis. 2014;9:161. [PMID: 25398587] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada (A.M.C., P.C., M.G.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L20-0015. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoBlindness Caused by a Junk Food Diet Rhys Harrison , Vicki Warburton , Andrew Lux , and Denize Atan Blindness Caused by a Junk Food Diet Rhys Harrison , Vicki Warburton , Andrew Lux , and Denize Atan Metrics Cited by58. Case Report 21 April 2020Volume 172, Issue 8Page: 575KeywordsCobalaminsInborn errors of metabolismMutationNutritionOptic neuropathyPrevention, policy, and public healthRetinopathyVisionVitamin B12 deficiencyVitamin D deficiency ePublished: 21 April 2020 Issue Published: 21 April 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".