Implantation of a Coronary Stent Into the Canaliculus of a Human Cadaver: A Pilot Study
Bibliographic record
Abstract
PURPOSE: To evaluate the possibility of implanting a drug-eluting coronary stent into the canaliculus of a human cadaver. METHODS: The punctum and canaliculus of an embalmed human cadaver were identified and dilated using a punctum dilator and Bowman probes. At this stage, the integrity of the lacrimal drainage system was assessed by dacryoendoscopy. A drug-eluting coronary stent, which was collapsed around a balloon at the tip of a catheter, was inserted into the canaliculus. The balloon was inflated to expand and lock the spring-like stent into position. The balloon catheter was then deflated and removed from the canaliculus. Dacryoendoscopy was used once again to assess the position of each stent after implantation. RESULTS: The four canaliculi of one human cadaver were successfully identified, dilated, and intubated using drug-eluting coronary stents. Dacryoendoscopy confirmed that each stent achieved a satisfactory position within the canaliculi. The seamless integration of the stent with the surrounding tissues resulted in a significant dilation of the canaliculi. The procedure was deemed short and simple, with the time required to implant a stent into the canaliculus and asses its position being less than a minute. CONCLUSIONS: Canalicular obstructions can often be a source of therapeutic challenges. Our pilot study shows that a drug-eluting coronary stent can be implanted with precision into the canaliculus of a human cadaver. We propose that at least some canalicular obstructions could be treated using a novel rigid mesh tube similar to drug-eluting coronary stents.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.002 |
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".