DIFFUSE UNILATERAL SUBACUTE NEURORETINITIS: A CASE OF MISTAKEN IDENTITY
Bibliographic record
Abstract
In Brief Background: The report details a case of diffuse unilateral subacute neuroretinitis (DUSN) wherein a subretinal parasite was visualized and subsequently destroyed with laser photocoagulation. Methods: Full historical and serologic investigations were carried out. A literature search to determine all possible causes of DUSN was also completed. Results: Serologic results supported Baylisascaris procyonis as the cause of infection, but imaging of the worm before destruction did not support this organism as the etiologic agent. On the basis of morphologic evaluation of still imaging and videoimaging, patient exposure information, and known causes of DUSN, the infection was likely due to Alaria species, providing further evidence of a trematode cause. Conclusions: The report adds to the literature that trematodes should be recognized as a possible cause of ocular larva migrans. Although laser therapy is appropriate and effective for both nematode and trematode infections of the eye, in the case of adjunctive medical therapy, identification of the parasite group is essential. The report details a case of diffuse unilateral subacute neuroretinitis likely due to Alaria species, providing further evidence of a trematode cause. Although laser therapy is appropriate and effective for both nematode and trematode infections of the eye, in the case of adjunctive medical therapy, identification of the parasite group is essential.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".