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Record W2893537956 · doi:10.1016/j.ijscr.2018.09.029

Trans-anal minimally invasive surgery

2018· article· en· W2893537956 on OpenAlexaff
Anne‐Marie Dufresne, Rebecca Withers, Jonathan Ramkumar, Shawn MacKenzie, George Melich, Elena Vikis

Bibliographic record

VenueInternational Journal of Surgery Case Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of British ColumbiaRoyal Columbian Hospital
Fundersnot available
KeywordsMedicineSurgeryRectumLesionAbdominal surgeryPort (circuit theory)Radiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Transanal minimally invasive surgery (TAMIS) is a valuable surgical option for removal of rectal polyps and early rectal cancers. A potential complication of this technique is abdominal entry if the lesion is located above the peritoneal reflection. We present the first case series describing the use of a laparoscopic stapling device to remove a sessile lesion, and seal the resulting defect simultaneously with full thickness excision of the rectal lesion, avoiding abdominal entry. PRESENTATION OF CASES: Five patients with rectal lesions between 8 and 14 cm from the anal verge are described in this case series. Each underwent a stapled-TAMIS procedure as the lesion was suspected to be above the peritoneal reflection. The goal specimen was achieved in each procedure. DISCUSSION: This article demonstrates the feasibility of a novel technique to remove sessile polyps in the upper rectum using laparoscopic staplers trans-anally through the TAMIS port. More studies and long-term follow-up are needed to evaluate the oncologic outcomes including the recurrence rate for those lesions removed with a stapler. CONCLUSION: For rectal lesions suspected to be above the peritoneal reflection, a stapled resection through a TAMIS port could prove be a valuable addition to the standard excisional approach to TAMIS.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.060
GPT teacher head0.329
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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