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Record W1503180963 · doi:10.1002/rcs.1500

Robotic‐assisted hepatic resection: a systematic review

2013· review· en· W1503180963 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 · 2013
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineResectionLaparoscopyRobotic surgerySurgeryGeneral surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Currently, hepatic resections are being performed with robotic-assisted systems. There is little evidence regarding the outcomes of this surgical approach. This study aims to systematically review the outcomes related to robotic-assisted hepatic resections. METHODS: A systematic search of electronic databases was completed. All human studies, limited to adults, published between 2000 to August 2011 were included. RESULTS: Eight studies yielded a total of 170 procedures. The overall morbidity rate was 11.6% (range 0-39%). There were no mortalities reported following robotic-assisted hepatic resection. Mean operative time was 264.8 minutes, with a mean hospital length of stay of 7.8 days. Rate of conversion was 6.6%. Cost was greater than either laparoscopy or open hepatic surgery. CONCLUSIONS: Our systematic review suggests robotic-assisted hepatic resection is safe and feasible, with low mortality and morbidity rates. Further research is needed to determine if oncological outcomes are similar.

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)0.002
metaresearch head score (Gemma)0.001
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.159
GPT teacher head0.342
Teacher spread0.183 · 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