Characterization of postoperative “fibrin web” formation after canine cataract surgery
Bibliographic record
Abstract
PURPOSE: To describe the occurrence and associated factors for "fibrin web" (FW) formation following phacoemulsification in dogs. METHODS: A retrospective review of medical records of all dogs undergoing phacoemulsification (MU-Veterinary Health Center, 2014-2018) was conducted to associate FW formation with signalment, systemic co-morbidities, cataract stage, surgeon (resident vs faculty), phacoemulsification time, IOL, and intracameral injections including viscoelastic type. Both univariate and multivariate statistical analyses were performed to evaluate associations among variables with FW formation. RESULTS: Data from 398 eyes on 201 dogs were included; 4 left eyes (4 dogs) developed presumptive endophthalmitis and were excluded from further analysis. Forty-eight eyes did not have cataract surgery. Hence, 350 eyes on 201 dogs were included in the analyses. Among these, 84 eyes (59 dogs) developed a FW. Univariate analyses showed that the odds of FW increased with age and phacoemulsification time. Additionally, FW web was associated lens type, lens brand, and viscoelastic type. Multivariate analyses showed that when comparing lens types in combination with a particular viscoelastic, viscoelastic impacted the estimated prevalence of FW formation the most. In contrast, when the data were analyzed by lens brand, lens brand impacted prevalence more than viscoelastic type. Diabetes mellitus was not associated with FW formation. CONCLUSIONS: Based on the available data, intraocular lens implantation, viscoelastic type, dog age, and phacoemulsification time were associated with FW formation. Diabetes mellitus, gender, cataract stage, surgeon, intracameral injections other than viscoeleastic, and intra- and postoperative complications were not associated with FW formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".