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ADVANCES IN THE ADJUVANT TREATMENT OF COLORECTAL CANCER

2006· review· en· W2043612111 on OpenAlexaff
Ina C. Nordman, Shoba Iyer, Anthony M. Joshua, Stephen Clarke

Bibliographic record

VenueANZ Journal of Surgery · 2006
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineColorectal cancerAdjuvantOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There have been numerous advances in the adjuvant therapy of colon cancer in the last two decades. METHODS: This review outlines the historical perspectives of adjuvant treatment as well as current and emerging standards of care. RESULTS: Although previous regimens included a variety of equivalent schedules of 5-fluorouracil and folinic acid, integration of newer drugs such as oxaliplatin are offering significant improvements in disease-free survival. The use of targeted agents such as bevacuzimab creates the potential to further increase cure rates in the adjuvant setting. The current low rate of referral of eligible patients for chemotherapy in Australia is also discussed. CONCLUSION: Adjuvant therapy for colon cancer is making major strides as we attempt to cure more patients.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.105
GPT teacher head0.404
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2006
Admission routes1
Has abstractyes

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