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Determinants of permanent liver limited disease (pLLD) in metastatic colorectal cancer (mCRC).

2023· article· en· W4379282265 on OpenAlexaff
Francesc Salvà, Cristina Dopazo, René Adam, Hugo P. Marques, Darius F. Mirza, Alessandro Ferrero, Felice Giuliante, Gernot Kaiser, Réal Lapointe, Marek Krawczyk, Maximiliano Gelli, José Guilherme Tralhão, Santiago López‐Ben, Irinel Popescu, Helena Isoniemi, Catherine Hubert, Vladimir Evgenievich Zagainov, Axel Andrès, Elena Élez

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsHôpital Saint-Luc
Fundersnot available
KeywordsMedicineColorectal cancerInternal medicineDiseaseMetastasisMultivariate analysisOncologyLogistic regressionUnivariate analysisImputation (statistics)Liver diseaseCancerGastroenterologySurgeryMissing data

Abstract

fetched live from OpenAlex

3561 Background: CRC is a complex and heterogeneous disease, with the liver being the most frequent site of metastasis. Around 20% of patients will always progress exclusively in the liver. These patients may be candidates for more aggressive therapeutic procedures that will impact in their outcome. LIVERMET SURVEY Database (LMSD) is a prospective international database, focused on patients operated for CRC liver metastasis, whether resected or not, which purpose is to evaluate patient outcomes and prognostic factors for these resected patients. We propose to analyse all patients enrolled in the (LMSD) to better characterised those determinants that are associated with pLLD. Methods: We analyzed all patients included in the LMSD. Patients with relapse of their disease after the first liver surgery were selected. In order to test associations between hepatic only and extrahepatic metastasis, a univariate and multivariate logistic model was performed with the variables considered clinically more relevant. Imputation of missing data using the mice method (Multivariate Imputation via Chained Equations) was performed. All analyses were performed with R 4.1.1 software. Results: A total of 8715 patients out of 33581 (26%) included in the LMSD presented disease recurrence after a first liver surgery. During their oncological history, pLLD occurred in 1392 patients (16%), and extrahepatic relapse was presented in 7323 patients (84%). The characteristics of patients with pLLD were as follows: 58.4% patients presented synchronic disease, 20.2% had right sided primary tumor, 58.6% presented unilateral disease at time of first hepatic surgery and 20.4% of patients presented R1 surgery. In multivariant analysis, right sided and rectum (HR 0.82. p = 0.001), unilateral liver involvement (HR 0.74, p < 0.001) and > 3cm of diameter in greatest lesion with maximum of 3 lesions (HR 0.85, p = 0.02) were predictive determinants of extrahepatic disease. Only synchronic metastases (HR 1.29, p < 0.001) and male sex (HR 1.15, p = 0.028) were associated with pLLD. Molecular information according to RAS, BRAF and MSI status was not evaluable in a majority of patients since this item has been recently implemented in the questionnaire. Conclusions: This study confirms that about 16% of patients with LLD mCRC will be pLLD mCRC. Despite clinical determinants like synchronic metastatic disease, which is associated with pLLD, further analyses including molecular determinants are needed. Identifying those determinants of pLLD in a scenario where more extreme surgeries and liver transplantations are being considered is a great challenge that needs to be addressed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.286
GPT teacher head0.462
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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