Clinicopathological Correlates: Chronic Arsenic Toxicity Causing Bilateral Symmetric Progressive Optic Neuropathy
Bibliographic record
Abstract
A 70 year-old man presented with insidiously progressing central visual acuity loss in both eyes over several years. Objectively the only abnormality identified on the exam was questionable granularity in the fovea in each eye. Extensive work up which included neuro-imaging, screening blood work for toxic and nutritional causes of optic neuropathy as well as electroretinogram and fluorescein angiography to rule out subtle maculopathy was all unrevealing. When vision continued to deteriorate over the next several years investigations were repeated and again did not yield any positive results. Levels of heavy metals were then obtained after further progression of visual loss, revealing very high levels of arsenic. Subsequent investigations revealed that patient has been spending almost every weekend for the past 28 years alone at a remote country cottage where the sole supply of water was from the local well. He also recalled that 1.5 months after purchasing the cottage he developed hemorrhagic colitis requiring partial colectomy. The specimen from colectomy was located and total reflection x-ray fluorescence testing performed in a specialized lab revealed greatly increased level of arsenic particle in the colonic biopsy from 28 years ago. This case is a reminder that heavy metal toxicity should be considered in a differential diagnosis of patients with bilateral symmetric optic neuropathy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".