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Record W1870194631 · doi:10.1002/rcs.432

Robotic‐assisted colon and rectal surgery: a systematic review

2011· review· en· W1870194631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2011
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of AlbertaRoyal Alexandra HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineColorectal surgeryRobotic surgerySurgeryGeneral surgeryColorectal cancerBody mass indexInclusion (mineral)Abdominal surgeryInternal medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Colorectal surgery is one of the most common procedures performed by general surgeons, with an increasing number being performed laparoscopically. Robotic technology is emerging in the ongoing evolution in minimally invasive surgery. This study systematically reviews the literature regarding the safety and feasibility of robotic-assisted colorectal surgery. METHODS: A comprehensive search of electronic databases was completed for the period 2000 to 2010. Two independent reviewers assessed the studies for relevance and inclusion, and extracted data. RESULTS: After an initial screen of 347 titles, 20 studies met the inclusion criteria. A total of 854 patients were included with a mean age of 61 years and a body mass index of 25.5 kg/m(2) . Major complications included 27 anastamotic leaks (27/766 = 3.5%), 10 post-operative bleeds (1.1%) and 14 post-operative infections (1.6%). There were no mortalities reported. CONCLUSIONS: This systematic review demonstrates that robotic-assisted colorectal surgery is emerging as a safe and feasible option in colorectal surgery.

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.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.088
GPT teacher head0.358
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