Bibliographic record
Abstract
We read with interest the elegant article by Dubost et al. 1documenting a correlation between increased optic nerve sheath diameters and preeclampsia. At Bascom Palmer Eye Institute we have been studying the potential application of sonography for ophthalmic regional anesthesia.The application of sonic energy around the eye is not without risk. Thermal and mechanical bio-effects are well described. Multiple international regulatory authorities, including the U.S. Food and Drug Administration2and Health Canada* have imposed stricter physical parameters for the use of ophthalmic ultrasound. In particular, limits on Mechanical Index and Thermal Index have been reduced to 0.23 and less than 1, respectively.We recently published a rabbit model study that compared thermal and mechanical changes induced by exposure to ophthalmic- and nonophthalmic-rated transducers.3Our data showed significant changes in intraorbital temperature after exposure to the nonorbital rated Sonosite Micromaxx 6-13 MHz linear transducer (Bothell, WA).Great benefit may emanate from intra- or perioperative ultrasonic ocular examinations, whether for optic nerve sheath diameters, regional anesthesia, or other applications. Investigators must remain cognizant of the potential deleterious ocular effects of sonic energy, and ensure that only orbital-approved technology is used in future research.†University of Miami, Miller School of Medicine, Miami, Florida. hpalte@med.miami.edu
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".