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Record W2462156241

Retroperitoneal sarcomas - A retrospective study

2009· article· en· W2462156241 on OpenAlexaboutno aff
Mohammed Saad Alqahtani, Alhasan Asiri

Bibliographic record

VenueBiomedical Research-tokyo · 2009
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBiopsySurgeryUnivariate analysisRetrospective cohort studySarcomaResectionSoft tissue sarcomaSurvival analysisSoft tissueRadiologyMultivariate analysisInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Outcomes of the management of retroperitoneal sarcoma in the Northern Alberta (NA), Canada was examined in this study. 75 patients (34 females and 41 males, age range 30-87 years) underwent operations at the CCI for PRS (sarcomas represented 61% of all the cases). A retrospective analysis of these patients was performed to determine the prognostic parameters associated with a favorable prognosis. Complete resection was possible in 33% of cases (n = 25), incomplete resection was performed in 35% (n = 26) and in 32% (n = 24) only biopsy was possible. Frequently resected adjacent organs were: kidney (15%), colon (10%) and pancreas (6%). Univariate analysis demonstrated that complete resection was an independent factor for survival as compared to partial resection or biopsy alone (p=0.001). Patients with complete resection had a 12 month survival of 100% (n = 25) compared to 84% (n = 22) for those undergoing partial resection and 25% (n = 5) for those with simple biopsy. A 24-month survival of the patients undergoing complete resection was 88% (n = 22). Median survival for type of surgical treatment was 91.2 (88.2-104.9) months for complete resection compared to 30.4 (24.2-41.5) months for partial resection and only 5.7 (2.6-8.1) months for biopsy. Complete resection is the cornerstone of the treatment and is important for long-term survival in patients with retroperitoneal soft tissue sarcomas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.090
GPT teacher head0.428
Teacher spread0.337 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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