Robot‐assisted minimally invasive lung brachytherapy
Bibliographic record
Abstract
BACKGROUND: This paper presents a novel alternative for the treatment of lung cancer. The method consists of accessing the lung through small incisions in a minimally invasive manner in order to insert radioactive seeds directly into the lung using a robotic surgical system. METHODS: An experimental test-bed to evaluate the feasibility of this approach has been developed. It consists of two surgical robotic systems, a device specifically designed to robotically implant radioactive seeds, needle tracking software, ultrasound imaging, electromagnetic tracking, and a surgical box that mimics a patient's thorax. A detailed comparison has been performed between currently available access options and robot-assisted minimally invasive access. RESULTS: The results show insignificant differences in accuracy between the methods, with the exception of a significant improvement when electromagnetic (EM) guidance was added to the non-robotic techniques. The navigation system reduced the number of attempts for all seed delivery methods. Significant reductions in time were achieved in the minimally invasive procedures by the addition of EM guidance. CONCLUSIONS: The performance achieved when using robotic systems and image guidance for minimally-invasive brachytherapy is clinically comparable to that achieved in an open surgery procedure, while reducing the invasiveness of the procedure, improving ergonomic conditions for the clinician and reducing radiation exposure.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".