Bibliographic record
Abstract
Soft-shell techniques exist for lower viscosity dispersive with higher viscosity cohesive ophthalmic viscosurgical devices (OVDs) (soft-shell technique [SST]), viscoadaptive OVDs with balanced salt solution (ultimate soft-shell technique), intraoperative floppy-iris syndrome (soft-shell bridge), and many specific modifications for disinserted zonular fibers, frayed iris strands, Fuchs endothelial dystrophy, small holes in the posterior capsule with protruding vitreous, capsular dye use, and others. Soft-shell techniques exist because it is rheologically impossible to control the surgical environment with a single OVD as well as with an ordered combination of rheologically different OVDs. Surgeons frequently confuse these techniques because of their multitude. This paper unifies all SSTs into a single improved tri-soft shell technique (TSST), from which basic specific applications to unusual circumstances are simple and intuitive. As shown with previous SSTs, the TSST allows surgeons to perform complex tasks with greater surgical facility and to protect endothelial cells better than with single OVDs. Financial Disclosure Dr. Arshinoff has acted as a paid consultant to many global ophthalmic viscosurgical device manufacturers, including all of those whose products are referred to in this article. Neither author has a financial or proprietary interest in any material or method mentioned.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".