Bibliographic record
Abstract
Despite their use in ophthalmology for more than a century, there are no fundamental biophysical models that account for the dissolution rate of intra-ocular gas bubbles in vivo, as a function of their initial size and composition. Any physical model that reliably predicts the evolution of these bubbles would be extremely useful for planning treatment, by predicting their size over time, and their periods of clinical usefulness. A fundamental, computational, biophysical model is developed here with these purposes in mind. The model is fundamental in the sense that the underlying rate equations are derived using linear response theory, wherein the driving force is a solute chemical potential difference. It is computational because the rate equations for the bubble volume and composition are fully coupled and must be integrated numerically and simultaneously. It was rendered clinically relevant by fitting its two adjustable parameters (two rate constants) to recently acquired live human eye data on intra-ocular air bubble dissolution rates. The model consists of an explicitly four-component, dome-shaped gas bubble, comprised of N2, O2, H2O and CO2, which exchanges N2 and O2 with circulating venous blood in the capillaries of the eye. The calibrated model provided an excellent fit to the available data, and interpolated nicely through three points not used for the calibration. It was used to show that by selecting different N2/O2 ratios in the initially injected gas, one can generate intra-ocular gas bubbles with significantly different predicted periods of clinical usefulness. This potentially useful predictive capability does not exist in current clinical practice.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".