3D technology in surgery of lung hydatid cyst
Bibliographic record
Abstract
We present the results of using 3D technology to improve visualization and planning of the lung hydatid cyst (LHC) surgery in a 12-year-old girl. The general physical examination was unremarkable. The 3D reconstruction of the lungs was performed based on CT DICOM files. The most reliable data were obtained with CT with an Air and Fat filter. Based on the performed 3D reconstruction, a multiport thoracoscopic pulmonary echinococcectomy was performed. Thoracoscopy revealed an LHC in the left lung’s posterior basal segment. Using a linear cutter stapler with two staple leg lengths of 45 mm/60 mm, cyst excision was performed. At the same time, it was possible to reduce the intervention volume and eliminate the bronchial fistula leading to the LHC. Therefore, applying AI technology to the left lung with a parasite cyst allowed for more precise visualization and successful LHC surgery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".