RECTAL ADENOCARCINOMA: CLINICAL EVALUATION AND TREATMENT THROUGH ROBOTIC SURGERY
Bibliographic record
Abstract
Rectal adenocarcinoma is a malignant neoplasm that develops in the glandular cells of the rectum. Its clinical evaluation and treatment have been objects of considerable interest, especially with the advancement of robotic surgery. The introduction of the robot in colorectal surgery has provided significant advantages, such as better visualization, precision and control of movements, resulting in potential benefits for patients. However, the literature on the effectiveness and outcomes of robotic surgery in the treatment of rectal adenocarcinoma is vast and varied. Objective: to examine and synthesize the available evidence on the clinical assessment and treatment of rectal adenocarcinoma using robotic surgery, focusing on the last 10 years. Methodology: The methodology followed the PRISMA checklist guidelines. We used the PubMed, Scielo and Web of Science databases to identify relevant articles published in the last 10 years. The descriptors used were "rectal adenocarcinoma", "robotic surgery", "clinical evaluation", "treatment" and "results". The inclusion criteria were studies that evaluated robotic surgery in the treatment of rectal adenocarcinoma, published in English or Portuguese. The exclusion criteria were studies unrelated to the topic, studies without access to the full text and studies with duplicate data. Results: The results revealed an increasing trend in the use of robotic surgery for the treatment of rectal adenocarcinoma. Key topics covered included oncological outcomes, postoperative complications, post-treatment quality of life, and comparisons with other surgical approaches. Conclusion: The review highlights the growing evidence supporting the efficacy and safety of robotic surgery in the treatment of rectal adenocarcinoma. However, additional studies are needed to further understand its clinical utility and its long-term impact on patient outcomes.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".