239. HYBRID ENDO-THORACOSCOPIC ENUCLEATION OF A GIANT OESOPHAGEAL LEIOMYOMA: A CASE REPORT AND LITERATURE REVIEW
Bibliographic record
Abstract
Abstract He was born and grew up in Penang. Attained MBBS from Aimst University, 2011, MRCS from Royal College of Ireland, 2014 and Doctor of General Surgery, from the National University of Malaysia (UKM), 2021. Currently pursuing his fellowship in Upper GI programme, under the Ministry of Health, Malaysia. Working experience in Hospital Duchess of Kent, Sandakan, Hospital Queen Elizabeth, Kota Kinabalu (both in Sabah) and Penang General Hospital until present. Active in hiking, running and football. Video Descrption The current standard of treatment of symptomatic giant oesophageal leiomyoma (OL) is surgical enucleation via minimal access technique. Our case demonstrated the safe enucleation of a giant mid-oesophageal OL—the largest size (13 cm) enucleated thoracoscopically—located in close proximity to the right pulmonary artery, aorta and right bronchus by Endoscopic-assisted Thoracoscopic surgery perfomed by a senior surgeon at a centre with cardiothoracic surgery team. Pre-operatively, OLs involving the great vessels should be discussed in an MDT involving the cardiothoracic surgery team as there is a possible need for CPB should the great vessels be injured or if there is a need for the heart to be collapsed to ease dissection. Patients should be adequately counselled of the potential risks and the need for more complex surgical procedures should intra-operative complications arise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".