Identification of Anomalous Features of Intravitreal Injections Using Micro-Computed Tomography
Bibliographic record
Abstract
PURPOSE: To identify anomalous features that impact drug delivery in the eye as a result of intravitreal injections using micro-computed tomography imaging. METHODS: Three-dimensional micro-computed tomography images were acquired following an intravitreal injection of 0.03 mL of contrast agent into ex vivo porcine eyes (n = 24). A baseline scan was acquired prior to injection to detect any abnormalities in the eyes. Acquisition continued at various time intervals up to 230 min post-injection. RESULTS: Air bubbles were clearly visible within the vitreous of 21 eyes following injections. There was a total of 36 air bubbles in the 21 eyes and the volume of the air bubbles ranged from 0.01 µL to 1.50 µL. It was found the size of the air bubbles decreased over the scanning period. Furthermore, many of the injected boli in the eye specimens did not have the commonly assumed spherical shape; rather, a variety of other shapes resulted. CONCLUSION: The presence of air bubbles and inconsistent bolus shapes have indicated that intravitreal injections have high variability. It is only through the realization of these anomalous features that the efficacy of intravitreal drug delivery will be improved through a consistent and accurate injection technique.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".