Video-assisted thoracoscopic resection of a giant esophageal schwannoma
Bibliographic record
Abstract
INTRODUCTION AND IMPORTANCE: Intrathoracic schwannomas are rare and difficult to diagnose. However, they are the most common type of neurogenic tumor in the chest. Most patients are incidentally diagnosed or develop symptoms from mass effect, such as chest pain, dysphagia or dyspnea. Larger tumors have been resected using open approaches, while smaller ones are often excised with minimally invasive approaches. CASE PRESENTATION: A 60-year-old woman with a prior Roux-en-Y gastric bypass and a history of dysphagia, decreased appetite, and weight loss was referred for evaluation. CT chest revealed an 8 cm soft tissue mass centered in the distal esophagus. Gastroscopy showed the tumor to be 8 cm as well, with 2 cm of normal esophagus prior to the gastric pouch. A right-sided video-assisted thoracoscopic (VATS) approach for enucleation was successfully completed with primary esophageal repair for an 8.0 × 5.5 × 6.5 cm schwannoma. CLINICAL DISCUSSION: Surgical resection for schwannomas is often indicated due to symptoms from mass effect (Moro et al., 2017). There are reports of VATS and robotic-assisted thoracic surgery approaches for small tumors. These techniques are appealing due to shorter length of stays and less post-operative pain. None have been described for lesions larger than 6 cm. CONCLUSION: Minimally invasive approaches such as VATS for large schwannomas are technically feasible and safe to perform without the need for a thoracotomy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".