AB046. The retinoblastoma model for translational research
Bibliographic record
Abstract
Background: Our national collaborative research initiative is proposing to develop a common infrastructure for Rb research. We are proposing a novel in vivo Rb model using human Rb cells line. Methods: The rabbit model has advantages over the mouse models: (I) the larger eye size of rabbits, similar to the human infant eye, permits a more accurate injection of the drugs and evaluation of methods of targeted intraocular drug delivery; (II) the rabbit model demonstrated similar fundus appearance and pathologic features to human Rb, including vitreous seeds of viable tumor when the retinal tumor is mid-sized, which are usually found in the late stage in mouse models. The lack of ability to eliminate vitreous seeds is a major reason of current treatment failures in Group C and D tumors; therefore, the rabbit model of Rb may be used as a model to evaluate the effectiveness and various routes of drug delivery. Results: This is an implementation of an infrastructure for evaluating therapeutic targets. In addition, this finding enables a variety of pharmacokinetic studies, pharmacodynamic and toxicology studies for new therapeutic agents. Conclusions: This infrastructure meets the growing concern of practitioners and researchers in the field. The common facility is easily accessible to all VHRN members on request, including requests from other sectors.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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 teacher head, 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".