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Record W7099539123

doi:10.1155/2012/708936 Research Article Uptake and Tolerance of Chemotherapy in Elderly Patients with

2012· article· en· W7099539123 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsChemotherapyCancerLung cancerDiseaseSurvival analysisProportional hazards model
DOInot available

Abstract

fetched live from OpenAlex

Copyright © 2012 Stacey Fisher et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The treatment of elderly cancer patients is complicated by many factors. We sought to assess the uptake and tolerance of chemotherapy among patients 75 years and older diagnosed with small cell lung cancer (SCLC) in years 2004–2008 in Alberta, Canada, and assess their survival. All patients who met the above criteria and had an oncologist-consult were included. Data were obtained from the Alberta Cancer Registry and chart review. A total of 171 patients were included in the study, 117 (68%) of whom began chemotherapy. Of those, 52 % completed all cycles, 66 % did not have any dose reductions, and 31 % completed all cycles at the recommended dose. The risk of death for patients who did not complete all cycles of chemotherapy was 2.72 (95% CI: 1.52–4.87) and for those who completed all cycles but with a reduced dose was 1.02 (95 % CI: 0.57–1.82) relative to those who completed chemotherapy at full dose after adjusting for several demographic/clinical factors. Our results suggest that a significant proportion of elderly patients are able to tolerate chemotherapy and receive a survival benefit from it while those who experience toxicity may receive a survival benefit from a reduction in chemotherapy dose as opposed to stopping treatment. 1.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.263
Teacher spread0.222 · 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.

Study designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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