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Record W4388862431 · doi:10.4081/aiua.2023.11723

Endophytic to total tumour volume ratio: An added variable to patients with T1b/T2 renal tumours undergoing partial nephrectomy

2023· article· en· W4388862431 on OpenAlexaff
Asmaa Ismail, Vahid Mehrnoush, Amer Alaref, Radu Rozenberg, Hazem Elmansy, Walid Shahrour, Nishigandha Burute, Anatoly Shuster, Owen Prowse, Ahmed S. Zakaria, Walid Shabana, Ahmed Kotb

Bibliographic record

VenueArchivio Italiano di Urologia e Andrologia · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsNOSM UniversityThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsNephrectomyMedicineRadiological weaponVolume (thermodynamics)KidneyUrologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Partial nephrectomy is the standard of care to patients with small renal masses. It is still encouraged to larger tumours whenever feasible. The aim of this study is to look for the endophytic to total tumour volume ratio as an added variable to study the complexity of partial nephrectomy to patients with T1b/ T2 renal tumours. METHODS: Retrospective data collection of patients that had partial nephrectomy for T1b/T2 renal tumours by a single surgeon was done. Radiological re-assessment for the CT images to measure the endophytic to total tumour volume ratio was done. RESULTS: The mean age of the patients was 63 years. The study included 25 males and 11 females. All cases were managed by open surgery using retroperitoneal transverse lateral lumbotomy and warm ischemia was used in all patients. The mean tumour volume was 74 cc, the mean endophytic tumour volume was 29 cc. The mean percentage of endophytic to total tumour volume was 42%. CONCLUSIONS: Partial nephrectomy is safe for most of the patients with good performance status, having large renal masses. More complex surgery can be predicted in patients with endophytic to total tumour volume greater than 42%.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.236
Teacher spread0.221 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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