A “domino-effect” case of multiple ocular pathologies ascribed to various complex causes
Bibliographic record
Abstract
This is a learning case targeted at medical students and other healthcare professionals who wish to improve their understanding of ophthalmic conditions and their anatomical underpinnings.This report will introduce a patient with a complex history of ocular pathologies who presented to a small urban community ophthalmology clinic.A brief summary of past medical history will be presented, followed by a detailed explanation of the symptoms, etiologies, and chosen treatments for the patient’s various conditions (iris and chorioretinal colobomas, uveitis, cataracts, retinal detachments, corneal edema, and glaucoma). In this case, the various medical interventions for our patient’s initial problems often led to complications and exacerbations of pre-existing conditions; therefore, special attention will be paid to highlighting both the pathophysiological and mechanical influences that may lead to, and exacerbate, eye disease.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".