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Record W194497992 · doi:10.1016/j.jcrs.2005.05.033

Diffuse lamellar keratitis complicating laser in situ keratomileusis

2005· article· en· W194497992 on OpenAlexaffabout
Mark Bigham, Charmaine L. Enns, Simon Holland, Jane A. Buxton, David Patrick, Steve Marion, Douglas W. Morck, Melynda Kurucz, Vania Yuen, Vanessa Lafaille, Jack O. Shaw, Richard Mathias, Morris Vanandel, Shaun Peck

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2005
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaCanadian Blood Services
Fundersnot available
KeywordsLASIKKeratomileusisMedicineOutbreakIncidence (geometry)Eye diseaseOptometrySurgeryConfidence intervalOphthalmologyCorneaInternal medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: To describe a surveillance system and summarize data between January 2000 and December 2002 regarding diffuse lamellar keratitis (DLK), a complication of laser in situ keratomileusis (LASIK) surgery. SETTING: Community-based clinics in British Columbia, Canada, in which LASIK surgery is performed. METHODS: Monthly, all clinics in which LASIK is performed reported the number of LASIK procedures and nonnominal cases of DLK (by grade and onset date) to the British Columbia Centre for Disease Control. Diffuse lamellar keratitis outbreaks were investigated, and prevention and control measures were recommended. RESULTS: From 2000 to 2002, approximately 72,000 LASIK procedures were performed, with a mean DLK incidence rate of 0.67% (95% confidence interval, 0.61-0.73). The overall proportion of DLK cases attributed to outbreaks was 64%, decreasing from 72% in 2000 to 40% in 2003. CONCLUSIONS: An effective DLK surveillance program was implemented at all laser refractive clinics in British Columbia. Reported DLK incidence was 0.67 cases per 100 procedures, with 64% occurring in outbreaks.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.566

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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

Citations17
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cataract & Refractive SurgerySame topicOcular Infections and TreatmentsFrench-language works237,207