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Record W1966006926 · doi:10.1097/ico.0b013e3181af3954

Post-traumatic Fungal Keratitis Caused by Carpoligna sp.

2010· article· en· W1966006926 on OpenAlexaff
Hall F. Chew, Donald Jungkind, Dean Mah, Irving M. Raber, Adam D. Toll, Mindy Tokarczyk, Elisabeth J. Cohen

Bibliographic record

VenueCornea · 2010
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsVoriconazoleNatamycinFungal keratitisKeratitisMedicineAmphotericin BPerforationSurgeryMicrobiologyDermatologyBiologyPathologyAntifungal

Abstract

fetched live from OpenAlex

PURPOSE: To report the first case of fungal keratitis caused by presumed Carpoligna species. METHODS: A 37-year-old gardener sustained a full-thickness, stellate corneal laceration while cutting wood outdoors with a circular saw. Two months after surgical repair, he developed a severe infectious keratitis with descemetocoele at the apex of the original stellate laceration. RESULTS: Culture results confirmed fungal elements without evidence of bacteria. Oral and topical voriconazole were initiated. Due to compliance and cost issues, voriconazole was replaced with natamycin 5% prior to discharge from hospital. The patient improved and healed without perforation. The patient was left with a central stromal scar. DNA extraction from the fungal colony allowed PCR amplification of the 28s ribosomal RNA region of the fungus that led to the diagnosis of Carpoligna pleurothecii. Corticosteroids were never used during the patient's treatment. CONCLUSION: This is the first reported case of infectious keratitis caused by presumed Carpoligna species. The treatment for Carpoligna pleurothecii keratitis includes voriconazole, natamycin, and possibly amphotericin B.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.257
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2010
Admission routes1
Has abstractyes

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