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Record W2087208556 · doi:10.1097/icb.0b013e3181238443

DIFFUSE UNILATERAL SUBACUTE NEURORETINITIS: A CASE OF MISTAKEN IDENTITY

2008· article· en· W2087208556 on OpenAlexaff
Kelly D. Schweitzer, Karen McClean, Kevin R. Kazacos, Raúl García

Bibliographic record

VenueRetinal Cases & Brief Reports · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineIdentity (music)

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.274
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations3
Published2008
Admission routes1
Has abstractyes

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