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Robotic Hysterectomy Strategies in the Morbidly Obese Patient

2013· article· en· W1999418677 on OpenAlexaboutno aff
Oscar D. Almeida

Bibliographic record

VenueJSLS Journal of the Society of Laparoscopic & Robotic Surgeons · 2013
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMorbidly obesePerioperativeLaparotomySurgeryBlood lossBody mass indexHysterectomyLaparoscopyProspective cohort studyUterosacral ligamentRobotic surgeryVaginaGeneral surgeryWeight lossObesityInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The purpose of this study was to present strategies for performing computer-enhanced telesurgery in the morbidly obese patient. METHODS: This was a prospective, institutional review board-approved, descriptive feasibility study (Canadian Task Force classification II-2) conducted at a university-affiliated hospital. Twelve class III morbidly obese women with a body mass index of 40 kg/m(2) or greater were selected to undergo robotic-assisted total laparoscopic hysterectomy. Robotic-assisted total laparoscopic hysterectomy, classified as type IVE, with complete detachment of the cardinal-uterosacral ligament complex, unilateral or bilateral, with entry into the vagina was performed. RESULTS: The median estimated blood loss was 146.3 mL (range, 15-550 mL), the mean length of stay in the hospital was 25.3 hours (range, 23- 48 hours), and the complication rate was 0%. The rate of conversion to laparotomy was 8%. The median surgical time was 109.6 minutes (range, 99 -145 minutes). CONCLUSION: Robotic-assisted total laparoscopic hysterectomy can be a safe and effective method of performing hysterectomies in select morbidly obese patients, allowing them the opportunity to undergo minimally invasive surgery without increased perioperative complications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2013
Admission routes1
Has abstractyes

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