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Retroperitoneoscopic living donor nephrectomy: initial experience with a unique hand‐assisted approach

2010· article· en· W1896492153 on OpenAlexaff
John-Paul Capolicchio, Andrew Feifer, Mark Plante, Jean Tchervenkov

Bibliographic record

VenueClinical Transplantation · 2010
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineNephrectomySurgeryAbdomenBlood lossLearning curveLaparoscopyKidneyInternal medicine

Abstract

fetched live from OpenAlex

The retroperitoneoscopic (RP) approach to live donor nephrectomy (LDN) may be advantageous for the donor because it avoids mobilization of peritoneal organs and provides direct access to the renal vessels. Notwithstanding, this approach is not popular, likely because of the steeper learning curve. We feel that hand-assistance (HA) can reduce the learning curve and in this study, we present our experience with a novel hand-assist approach to retroperitoneoscopic live donor nephrectomy (HARP-LDN). Over a one-yr period, 10 consecutive patients underwent left HARP-LDN with a mean body mass index of 29 and three with prior left abdomen surgery. The surgical technique utilizes a 7 cm, muscle-sparing incision for the hand-port with two endoscopic ports. Operative time was an average of 155 min., with no open conversions. Mean blood loss was 68 mL, and warm ischemia time was 2.5 min. Hospital stay averaged 2.7 d with postoperative complications limited to one urinary retention. Our modified HARP approach to left LDN is safe, effective and can be performed expeditiously. Our promising initial results require a larger patient cohort to confirm the advantages of the hand-assisted retroperitoneal technique.

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 score0.133
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.368
Teacher spread0.319 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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