Bibliographic record
Abstract
Oncological adequacy in rectal cancer surgery mandates not only a clear distal and circumferential resection margin but also resection of the entire ontogenetic mesorectal package. Incomplete removal of the mesentery is one of the commonest causes of local recurrences. The completeness of the resection is not only determined by tumor and patient related factors but also by the patient-tailored treatment selected by the multidisciplinary team. This is performed in the context of the technical ability and experience of the surgeon to ensure an optimal total mesorectal excision (TME). In TME, popularized by Professor Heald in the early 1980s as a sharp dissection through the avascular embryologic plane, the midline pedicle of tumor and mesorectum is separated from the surrounding, mostly paired structures of the retroperitoneum. Although TME significantly improved the oncological and functional results of rectal cancer surgery, the difficulty of the procedure is still mainly dependent on and determined by the dissection of the most distal part of the rectum and mesorectum. To overcome some of the limitations of working in the narrowest part of the pelvis, robotic and transanal surgery have been shown to improve the access and quality of resection in minimally invasive techniques. Whatever technique is chosen to perform a TME, embryologically derived planes and anatomical points of reference should be identified to guide the surgery. Standardization of the chosen technique, widespread education, and training of surgeons, as well as caseloads per surgeon, are important factors to optimize outcomes. In this article, we discuss the introduction of transanal TME, with emphasis on the mesentery, relevant anatomy, standard procedural steps, and importance of a training pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".