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Record W3203002280 · doi:10.2217/cer-2021-0013

Prognostic and predictive value of microsatellite instability status among patients with colorectal cancer

2021· article· en· W3203002280 on OpenAlexaff
Firas Baidoun, Anas M. Saad, Omar Abdel‐Rahman

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2021
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMicrosatellite instabilityInternal medicineColorectal cancerChemotherapyOncologyStage (stratigraphy)Proportional hazards modelPredictive valueMultivariate analysisCancerAdenocarcinomaMicrosatellite

Abstract

fetched live from OpenAlex

Objectives: Compare overall survival (OS) between microsatellite instability (MSI) high and MSI-stable and analyze the effect of chemotherapy on OS. Methods: National cancer database was queried for patients diagnosed with colorectal adenocarcinoma between 2010 and 2016. We evaluated the OS and the chemotherapy effect using Kaplan–Meier estimates and multivariate Cox regression analyses. Results: Total of 30,436 stage II patients and 30,302 stage III patients were included. In stage II with high-risk features and MSI-high, patients who received chemotherapy had better OS compared to patients who didn't receive chemotherapy. The same was found in stage II with no high-risk features and MSI-high group. Conclusion: Stage II colorectal cancer patients with high-risk features and MSI-high who received chemotherapy have better OS compared to patients who didn't receive chemotherapy.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.040
GPT teacher head0.382
Teacher spread0.341 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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