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Record W4399348295 · doi:10.1097/icb.0000000000001604

AGGREGATIBACTER APHROPHILUS AS A RARE CAUSE OF ENDOGENOUS ENDOPHTHALMITIS

2024· article· en· W4399348295 on OpenAlexaff
Pushpinder Kanda, Deeksha Kundapur, Henry Chen, Michael Dollin

Bibliographic record

VenueRetinal Cases & Brief Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineEndophthalmitisOphthalmology

Abstract

fetched live from OpenAlex

Purpose: To describe a rare case of endogenous endophthalmitis caused by Aggregatibacter aphrophilus ( A. aphrophilus ) in the context of all previously reported cases of endophthalmitis caused by this organism. Methods: A 59-year-old man with no history of ocular trauma or surgery presented with a red, painful right eye with light perception vision. Examination showed a hypopyon, dense fibrin reaction, and cells in the anterior chamber. B-scan ultrasound showed evidence of vitritis. Broad investigations were initiated for both inflammatory and infectious etiologies. Results: The patient was diagnosed with presumed endogenous bacterial endophthalmitis as the blood cultures were positive for A. aphrophilus and vitreous cultures were negative for growth. The patient was promptly treated with intravitreal and systemic antibiotics and later underwent pars plana vitrectomy. The final visual acuity was 20/50. The patient had a prosthetic aortic valve and careful investigations had ruled out endocarditis. Conclusion: This case emphasizes the importance of early recognition, investigation, and intervention for A. aphrophilus endophthalmitis including the role of therapeutic vitrectomy. While early presentation can mimic ocular inflammatory diseases, infection from underrecognized causative organisms like A. aphrophilus should be ruled out. Systemic and intraocular antibiotics should be started promptly to provide the best visual prognosis.

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)0.000
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 score0.042
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.030
GPT teacher head0.297
Teacher spread0.267 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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