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Record W1708402514 · doi:10.5489/cuaj.2572

Open adrenalectomy for medium sized adrenocortical tumour: How I do it?

2015· article· en· W1708402514 on OpenAlexvenueno aff
Wael Sameh, Ahmed Kotb

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsnot available
Fundersnot available
KeywordsAdrenalectomyAdrenocortical carcinomaBilateral adrenalectomyMedicineInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of our work was to report our experience in managing cases with medium-sized adrenocortical carcinoma by the high retroperitoneal extra pleural approach. METHODS: During the past 2 years, 10 patients with suspected adrenocortical carcinoma were managed by our technique: the high supra 10th rib, retroperitoneal extra pleural approach. We included cases with 5 to 10 cm adrenal masses, suspected as adrenocortical carcinoma. RESULTS: The mean patient age was 38 years (range: 26-44), the median tumour volume was 7 cm (range: 5-8). Of the 10 patients, 7 were female. Of the patients, 6 had right- and 4 had left-sided tumours. Intraoperatively, all cases had proper surgical removal, with no apparent residual tumour tissue. No single patient required a chest tube or developed respiratory problems. There were no major vascular injuries during surgery. We did not compare our findings to the standard lateral or subcostal approaches, as in our institution we adopt this high lateral approach for medium-sized tumours, while managing larger tumours with transperitoneal subcostal approach and smaller tumours laparoscopically. CONCLUSION: The high supra 10th lateral retroperitoneal, extra pleural approach is a safe, doable technique, allowing easy access to medium-sized suprarenal tumours and its vasculature, for cases suspected to be adrenocortical carcinoma.

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)0.002
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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)0.0010.000

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 head0.041
GPT teacher head0.286
Teacher spread0.245 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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