Editorial: Advances and novel technologies in surgical instruments for the treatment of cancer
Bibliographic record
Abstract
Advances and novel technologies in surgical instruments for the treatment of cancer Traditional surgery for patients with malignant diseases is increasingly supported by robotic systems, artificial intelligence, augmented reality and smart instruments.These new techniques help the surgeons to plan and perform the surgical task in a better or faster way.Although in the last years the technical development has produced a long list of excellent opportunities in this field, the majority fails to find their way in into the clinical routine.The reasons often include the questionable benefit for the patients, difficulties in handling the device in a feasible manner or overwhelming costs, which are not covered by the medical insurance.In addition, devices, which are regularly used for the treatment of patients in the clinic, often lack the verification of their benefit by randomized clinical trials.The goal of the special issue is to bring together technical developers and clinical physicians already in an early stage of development in order to discuss the technical potential and the clinical needs in the treatment of cancer patients.We believe that within every technical development, the patients' needs and the potential benefit for the treatment of their tumor burden should be in the center of interest.To this end, this special issue offers an open platform for both, technical and clinical contributions, which aim to improve the treatment of oncologic patients by the means of novel technical ideas.A series of the accepted manuscripts focus on endoscopic techniques in the field of surgery describing procedures in a variety of organs including liver, esophagus, vagina, parotid gland, rectum and prostate.This demonstrates that almost all surgical specialties have adapted to this form of visualization in some form.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".