Diffuse lamellar keratitis associated with tabletop autoclave biofilms: case series and review
Bibliographic record
Abstract
PURPOSE: To report a diffuse lamellar keratitis (DLK) cluster attributed to autoclave reservoir biofilm and to review the risk and prevention of DLK and toxic anterior segment syndrome (TASS) caused by such biofilms. SETTING: Refractive Surgery Center, University of California, Berkeley. DESIGN: Observational case-control study and review of literature. METHODS: Eyes were evaluated for DLK following laser in situ keratomileusis (LASIK) over a 5-year period. Multiple changes in surgical and operating room protocols were prompted by a cluster of DLK cases. The autoclave reservoir chamber wall was cultured for microbial contamination. The MEDLINE database was used to identify relevant past publications. RESULTS: From January 7, 2010, to December 18, 2014, 1115 eyes received LASIK. Between September 2, 2010, and June 11, 2012, 147 eyes of 395 LASIK cases developed DLK (37.2%). Systematic modifications in surgical protocols were unsuccessful in ending the prolonged cluster of DLK cases until the STATIM 2000 autoclave was replaced with a new STATIM autoclave and a reservoir sterilization and surveillance protocol implemented. Over the subsequent 30 months, DLK incidence was reduced to 2.2% (14 DLK cases from 632 total LASIK cases, P < .0001). The retired autoclave reservoir chamber wall cultures grew Pseudomonas aeruginosa and the Burkholderia cepacia complex. CONCLUSIONS: Fluid reservoirs of tabletop steam autoclaves can readily develop polymicrobial biofilms harboring microbial pathogens, whose inert molecular byproducts can cause DLK and TASS when introduced to the eye by surgical instruments. Stringent reservoir cleaning and maintenance may significantly reduce this risk by preventing and removing these biofilms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".