Squeezability of eye drop containers used in dry eye disease management
Bibliographic record
Abstract
Clinical relevance Ocular lubricants are the mainstay of dry eye management and are available in a variety of formulations and bottle designs. The squeezability of ocular lubricant bottles may influence administration and compliance.Background A key component for managing dry eye disease is the use of ocular lubricants. The different bottle types, each with their unique material strength and dispensing mechanism, may be challenging for patients with limited hand and pinch strength. The aim of this study was to evaluate the force required to expulse a drop from different eye drop containers used in the management of dry eye disease.Methods The force, in newtons (N), required to expulse a drop from different eye drops was evaluated five times using a force gauge, and divided by bottle type, i.e. tubes, unidose, multidose with preservatives, and multidose preservative free (MDPF) bottles.Results Sixty (n = 60) bottles were examined with 57 eye lubricants and 3 dry eye medications (cyclosporine (0.5% and 0.9%), and lifitegrast). The average force varied depending on the bottle type (tubes 4.28 ± 1.29 N; unidose 14.24 ± 4.83 N; multidose 16.62 ± 5.21 N; MDPF 26.68 ± 8.32 N, p < 0.001). Post-hoc test revealed that MDPF bottles required more force than all other bottle types (p < 0.001), and among those, the ophthalmic squeeze dispenser bottle required significantly more force (p < 0.001). Lifitegrast required more force (17.38 ± 2.13 N) than cyclosporine 0.5% (9.16 ± 0.80 N, p < 0.024) and cyclosporine 0.9% (5.68 ± 0.40 N, p < 0.001), but was not significantly different from unidose ocular lubricants (p > 0.05).Conclusion The squeezability of bottles used in dry eye disease management varies with bottle type. Hand and pinch strength should be considered when choosing products for dry eye disease management, as the squeezability of an ophthalmic drop can influence its administration and compliance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".