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Record W2950996255 · doi:10.1016/j.jcrs.2019.03.001

Successful management of severe post-LASIK Mycobacterium abscessus keratitis with topical amikacin and linezolid, flap ablation, and topical corticosteroids

2019· article· en· W2950996255 on OpenAlexaff
Cristina Bostan, Élise Slim, Johanna Choremis, Tanguy Boutin, Isabelle Brunette, Michèle Mabon, Julia C. Talajic

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineMycobacterium abscessusAmikacinLASIKKeratitisVoriconazoleLinezolidSurgeryTopical steroidKeratomileusisDermatologyAntibioticsMycobacteriumVancomycinPathologyMicrobiologyVisual acuityStaphylococcus aureusTuberculosisAntifungalBiology

Abstract

fetched live from OpenAlex

This is a case report of post-laser in situ keratomileusis (LASIK) multidrug-resistant Mycobacterium abscessus keratitis managed with combined topical amikacin and linezolid, flap amputation, and corticosteroids. A 34-year-old woman presented with a corneal interface infiltrate 3 weeks after LASIK. Cultures isolated mycobacteria. The infiltrate did not improve under intensive topical therapy and interface irrigation with empiric antibiotics over 5 weeks, and the infiltrate progressed to severe inflammation and stromal neovascularization. After identification of M abscessus susceptible only to amikacin and linezolid, antimicrobials were adjusted and the flap was ablated. Cultures repeated 1 week later came back negative. However, stromal inflammation and neovascularization persisted. Topical steroids achieved regression of the inflammation within 1 week. Identification of the mycobacterial pathogen and its susceptibilities is essential given the possibility of multidrug resistance. Topical linezolid can be effective in susceptible species. Corticosteroids can be helpful in cases with severe inflammation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.260
Teacher spread0.251 · 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 designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

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