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Record W2013983554 · doi:10.1089/lap.2006.0027

Laparoscopic Nephrectomy in a Patient with Ankylosing Spondylitis: Surgical and Anesthetic Challenges

2007· article· en· W2013983554 on OpenAlexaff
Raghuram Sampath, Bikram Raychaudhuri, Arun Sahai, Helena Scott, Prokar Dasgupta

Bibliographic record

VenueJournal of Laparoendoscopic & Advanced Surgical Techniques · 2007
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineSurgeryAnkylosing spondylitisNephrectomyPhysical examinationComplicationKidney

Abstract

fetched live from OpenAlex

A 55-year-old man with ankylosing spondylitis was referred with left sided loin pain, loin mass, and painless macroscopic hematuria. Physical examination revealed a palpable loin mass, fixed flexion deformity of the lumbar and cervical spines, with severely restricted cervical movement and mouth opening. An ultrasound and computed tomography scan confirmed a 7-cm solid mass in the left kidney. Following a multidisciplinary meeting he elected to undergo radical laparoscopic nephrectomy. An anesthetic opinion was sought in view of the expected difficulties with intubation. Mouth opening was restricted to 3 fingers and he was Mallampati grade 3 on airway examination. As the degree of spinal flexion deformity and restricted spinal movement was significant, the patient was placed in a lateral decubitus position, and surgery was performed using a transperitoneal approach. A five-port technique was employed and was carried out successfully with no complication. Operative time was 240 minutes and estimated blood loss was 700 mL. His postoperative inpatient stay was 4.5 days. Surgical margins were clear and the patient was disease-free at 2-year follow-up. Laparoscopic nephrectomy in a patient with ankylosing spondylitis is technically challenging for both the surgeon and the anesthetist, however, with the right preoperative planning, potential morbidity can be limited to ensure a good outcome for the patient.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.285
Teacher spread0.273 · 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 designNot applicable
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

Citations2
Published2007
Admission routes1
Has abstractyes

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