New classification of ophthalmic viscosurgical devices—2005
Bibliographic record
Abstract
PURPOSE: To revise the generally accepted classification of ophthalmic viscosurgical devices (OVDs) to include cohesion data and the new class of viscous dispersive OVDs. SETTING: York Finch Eye Associates, Toronto, Ontario, Canada, and Alcon Research Limited, Fort Worth, Texas, USA. METHODS: Pseudoplasticity and cohesion-dispersion (CDI) data of DisCoVisc (hyaluronic acid 1.6%-chondroitin sulfate 4%), a new viscous dispersive OVD, were determined and compared with existing OVDs. The existing classification of OVDs was unable to accommodate its properties, so the classification was modified to include a new class and other potential new classes which currently remain unoccupied. RESULTS: Current OVD classification, although based on the clinically significant rheologic parameters of zero-shear viscosity and cohesion, only uses zero-shear viscosity because of the high correlation of these 2 parameters in existing OVDs. The appearance of DisCoVisc forces modification of the existing scheme because it does not fit into a preexisting category. The new proposed broadened classification is changed from a 1-dimensional list into a 2-dimensional table and considers CDI independently from viscosity for all OVDs. Expansion of the classification of OVDs in this manner predicts further possible new innovative OVDs for surgical use. CONCLUSION: The surgical behavior of OVDs can be predicted by their position in a classification of OVDs based upon zero-shear viscosity and cohesion.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".