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Record W2141561675 · doi:10.5430/jst.v2n3p38

Adjuvant treatment of colorectal cancer in the elderly: Where do we come from and where are we going?

2012· article· en· W2141561675 on OpenAlexvenueno aff
Maria Di Bartolomeo, Filippo Pietrantonio, Pamela Biondani, Filippo de Braud

Bibliographic record

VenueJournal of Solid Tumors · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineColorectal cancerOxaliplatinInternal medicinePopulationCancerClinical trialOncologyRandomized controlled trialObservational study

Abstract

fetched live from OpenAlex

Objective: Colorectal cancer is the third most commonly reported cancer in the world and about 50% of patients are diagnosed over the age of 70 years. The authors discuss age-related changes in organ function, comorbidities and frailty in the elderly, and their impact on chemotherapy toxicity. Methods: The authors review data from observational studies and subgroup analyses of randomized clinical trials on adjuvant chemotherapy in elderly colorectal cancer patients. Results: Several large population-based studies suggest that adjuvant chemotherapy is offered less frequently to elderly patients, although in recent years the prescription patterns tended to significantly increase. In fact, data from retrospective analyses of randomized trials indicate that elderly stage III colorectal cancer patients may get similar clinical advantage from adjuvant treatment with fluoropyrimidines, although major comorbidities may substantially limit life expectancy and minimize the survival benefits. The use of oxaliplatin-based regimens need s to take into account the individual risk/benefit profile due to lack of unequivocal evidence of positive literature data. Conclusions: Adjuvant chemotherapy of colorectal cancer should be investigated by prospective trials specifically designed for the elderly. Fit elderly patients should be offered standard adjuvant treatments, while modified schedule, attenuated doses or even treatment omission can be offered to more frail 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.314
Teacher spread0.289 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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