Ocular Impression-Taking—Which Material Is Best?
Bibliographic record
Abstract
OBJECTIVES: To assess the efficacy and effect on clinical signs of a polyvinylsiloxane (Tresident; Shütz Dental Group GmbH, Germany) compared with an irreversible hydrocolloid (Orthoprint; Zhermack SpA, Badia Polesine, Italy) for ocular impression-taking. METHODS: Twenty subjects were recruited (13 female and 7 male), with mean age 31.1±4.6 years (SD) (range 25.8-39.7). Subjects attended for 2 sessions, each of 1-hr duration, on 2 separate days. Each session was scheduled at the same time on each day. At each visit, the subject underwent an ocular impression procedure, using either Tresident or Orthoprint, in random order and to one eye only. Investigator 2 was blind to this assignment. Two experienced practitioners conducted the study, investigator 1 performed the ocular impression procedures and investigator 2 observed and assessed the clinical signs: logMAR visual acuity, ocular surface staining, tear break-up time (TBUT), and ocular hyperemia. RESULTS: Visual acuity was unaffected by either material; TBUT was marginally disrupted by both materials, but was not clinically significant according to published criteria; ocular redness increased with both materials; and corneal staining was significantly greater after Orthoprint impression. Less redness and clinically insignificant staining after impression-taking, with fewer clinical complications, was found after use of Tresident. CONCLUSIONS: Tresident offers a quicker, more effective, and clinically viable method of obtaining ocular impression topography compared with the traditional Orthoprint, and Orthoprint causes significantly more superficial punctuate staining of the corneal epithelium than Tresident.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".