The benefit of smart phone usage in liver organ procurement
Bibliographic record
Abstract
A 56-year-old man was on the transplant list with end-stage liver disease secondary to hepatitis C when a donor liver became available at a location 545 km away. The procurement team, consisting of a senior and junior fellow, went on the retrieval, while the staff surgeon remained in the hospital with the recipient. At the time of organ procurement, a suspicious lesion was identified in the left lateral lobe. The transplant fellows took intraoperative pictures of the lesion with a smart phone and sent them to the staff surgeon for advice. A teleconsultation, facilitated by images sent from the smart phone, took place over the next 22 min. The decision was made to proceed with the transplant, as it was felt that the lesion could be resected from the liver allograft. Had the fellows not been able to interact with the staff surgeon in real-time during the surgery, there is a high likelihood that the organ would have been rejected by the staff surgeon due to the unexpected finding. The patient's postoperative course was relatively uneventful with no evidence of infection. The patient was discharged from hospital and continues to do well. We expect that the role of smart phones in remote consultation will continue to expand in future.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".