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Record W2891162529 · doi:10.1002/jso.25228

Assessing tools for management of noncolorectal nonneuroendocrine liver metastases: External validation of a prognostic model

2018· article· en· W2891162529 on OpenAlexaff
Melanie E. Tsang, Alyson Mahar, Guillaume Martel, Sean P. Cleary, Sulaiman Nanji, Jean‐François Ouellet, Roberto Hernandez‐Alejandro, Alice C. Wei, Julie Hallet

Bibliographic record

VenueJournal of Surgical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoWestern UniversityUniversité LavalUniversity of OttawaKingston Health Sciences CentreQueen's UniversityCentre hospitalier universitaire de QuébecUniversity of ManitobaHealth Sciences CentreUniversity Health NetworkOttawa HospitalSt Joseph's Health Centre
Fundersnot available
KeywordsMedicineCohortHazard ratioInternal medicineProportional hazards modelRisk assessmentPrognostic modelFramingham Risk ScoreResectionOverall survivalSurgeryConfidence intervalDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Selection criteria and benefits for resection of noncolorectal, nonneuroendocrine liver metastases (NCNNELM) remain debated. A prognostic score was developed by the Association Française de Chirurgie (AFC) for patient selection, but not validated. We performed a geographic external validation of this score. METHODS: Patients with resected NCNNELM from six institutions (2000-2014) were assigned risk groups based on the AFC score. Discrimination was evaluated by visually inspecting separation of overall survival (OS) curves among risk categories. The slope of the continuous score on OS and hazard ratios for risk categories were examined. RESULTS: Of 165 patients, 53 (32.1%) were low-risk, 85 (51.5%) intermediate-risk, and 27 (16.4%) high-risk. The OS curves did not separate among risk groups: 5-year OS were 60.1% (low), 57.1% (intermediate), and 55.6% (high). The parameter estimate (0.02) indicated lower discrimination than in the AFC cohort. Hazard ratios of 1.05 (0.63 to 1.70) for low vs intermediate, 0.87 (0.46 to 1.64) for low vs high, and 0.83 (0.46 to 1.49) for intermediate vs. high, demonstrated lack of discrimination in OS among risk groups. CONCLUSION: While long-term survival is achievable, discrimination of the AFC score is not maintained in a geographic external cohort of resected NCNNELM. It is not generalizable to this external population.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.088
GPT teacher head0.420
Teacher spread0.332 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

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