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Record W4213301883 · doi:10.1038/s41598-022-06696-5

A laboratory evaluation of nozzle tip damage in four generations of intraocular lens injector systems using a self-developed damage scale

2022· article· en· W4213301883 on OpenAlexaff
Hui Fang, Lu Zhang, Sonja Schickhardt, P. Merz, Weijia Yan, M. Leroux, Gerd U. Auffarth

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversité Laval
FundersUniversitätsklinikum Heidelberg
KeywordsInjectorNozzleMaterials scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

During intraocular lens (IOL) implantation it is not uncommon for the injector's nozzle-tip to get damaged. However, the damage has not been systematically described or evaluated using an objective scale. In this study we developed our own system-the Heidelberg Score for IOL Injector Damage ("HeiScore"), which was used to grade 60 injectors from four generations of injector models (Monarch III D, AcrySert C, UltraSert, AutonoMe) made by the same manufacturer. (Alcon Laboratories Inc.) HeiScore has six grades of nozzle-tip damage: no damage (which was graded 0); slight scratches (1), deep scratches (2), extensions (3), cracks (4) and bursts (graded number 5). The score for each injector model was the sum of all grades (total number), and we could compare the four injector models. The injectors showed varying damage profiles, from "no damage" to "crack". A tendency of a lower damage score in the newer generations of IOL injectors was noted. However, a statistically significant difference was observed only between Monarch III D and AutonoMe. The "Heidelberg Score for IOL Injector Damage" could efficiently and effectively evaluate the damage to IOL injector systems, which might help manufacturers optimize the positioning of the IOL in the injector during pre-loading.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.296
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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