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Record W2054676440 · doi:10.5539/cco.v2n2p70

Outcome of Multimodality Therapy for Elderly Colorectal Cancer Patients

2013· article· en· W2054676440 on OpenAlexvenueno aff
Yasuhiro Inoue, Yuji Toiyama, Koji Tanaka, Yasuhiko Mohri, Masato Kusunoki

Bibliographic record

VenueCancer and Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineColorectal cancerCancerChemotherapyMultimodalityInternal medicineRadiation therapyMultimodal therapyRegimenOncology

Abstract

fetched live from OpenAlex

The aim of this study was to analyze patterns of multimodality therapy in elderly patients with advanced colorectal cancer. We enrolled 272 patients with colorectal cancer. All patients received chemotherapy and some patients received secondary cytoreductive surgery and/or radiofrequency ablation. We compared differences between elderly patients (age >=75 years) and non-elderly patients (age <75 years), especially in relation to multimodality therapy. There were no significant differences in cancer-specific survival between elderly (n = 37) and non-elderly patients (n = 235).Twenty-seven percent of elderly and 35% of non-elderly patients received multimodality therapy, which resulted in prolonged survival. Although the main chemotherapy regimen was the same in both groups who received multimodality therapy, elderly patients who received chemotherapy alone seemed to be under-treated. For elderly patients, prognostic factors were host-related, such as comorbidities, whereas for non-elderly patients prognostic factors were tumor-related. Comorbidities and modified Glasgow Prognostic Score may be prognostic indicators in elderly patients receiving multimodality therapy. In conclusion, chronological age alone should not contraindicate multimodality therapy of colorectal cancer in elderly patients. Appropriate selection criteria for multimodality therapy in elderly patients should include not only tumor characteristics, but also host- and treatment-related factors.

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 score0.394
Threshold uncertainty score0.368

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 head0.098
GPT teacher head0.472
Teacher spread0.373 · 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
Published2013
Admission routes1
Has abstractyes

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