MétaCan
Menu
Back to cohort
Record W1992771140 · doi:10.1002/rcs.400

Robotic‐assisted bariatric surgery: a systematic review

2011· review· en· W1992771140 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
TopicBariatric Surgery and Outcomes
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineSurgeryRobotic surgeryBody mass indexAnastomosisWeight lossStenosisGeneral surgeryObesityInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Bariatric laparoscopic surgery has been shown to lead to sustainable weight-loss in obese individuals. Robotic-assisted laparoscopic surgery is proposed as the next major evolution in minimally invasive surgery. This study systematically reviews the literature regarding the feasibility and safety of robotic-assisted bariatric surgery in obese patients. METHODS: A comprehensive search of electronic databases was completed for the period 2003 to 2010. Two independent reviewers assessed the studies for relevance, inclusion, and extracted data. RESULTS: After an initial screen of 297 titles, 22 studies met the inclusion criteria. A total of 1253 patients with a mean preoperative body mass index of 46.6 kg/m(2) were obtained from 13 included studies. Major complications of malabsorptive procedures included eight anastomotic leaks (2.4%), bleeding (7/349 patients = 2%) and strictures/stenosis (13/430 patients = 3%). There were no reported deaths. CONCLUSIONS: This systematic review demonstrates that robotic-assisted bariatric surgery is both a safe and feasible option for severely obese patients.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.078
GPT teacher head0.337
Teacher spread0.259 · 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