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A prospective comparison of Karnofsky (KPS) with ECOG performance status in patients with non-small cell lung cancer (NSCLC): A COMET group study investigating sensitivity and specificity issues important in clinical decision making

2005· article· en· W2243951255 on OpenAlexaboutno aff
Patricia J. Hollen, Richard J. Gralla, John A. Stewart, C. Chin, G. A. Bizette, N. B. Leighl, Philip Kuruvilla, J. Meharchand, H. Solow

Bibliographic record

VenueJournal of Clinical Oncology · 2005
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicinePerformance statusLung cancerDocetaxelChemotherapyOncologyProspective cohort studyCancerStage (stratigraphy)

Abstract

fetched live from OpenAlex

8134 Background: Performance status (PS) continues to be among the most accurate predictors of survival in all patients with lung cancer. Prior studies confirmed that both ECOG and KPS methods are useful and correlate fairly well with each other. However, new findings and practice patterns have necessitated investigation to determine if differences relevant to clinical decision making exist between the two methods. Two key factors have emerged: 1) suitability for chemotherapy is often based on PS (0 - 1 ECOG or 80 - 100 KPS), and 2) recent trials correlating single nucleotide polymorphisms (SNPs) with chemotherapy resistance and survival have found PS to be an important determinant for specific mutations. Methods: 75 patients with advanced NSCLC were prospectively evaluated at 9 COMET sites in Ontario. Both the ECOG and KPS methods, as well as the LCSS-QL (a validated QL instrument) were given prior to receiving initial docetaxel + platinum chemotherapy. Eligibility: PS 0 - 2 ECOG and KPS ≥ 60, Stage III-IV. Descriptive methods and linear regression were used to explore sensitivity and specificity. Results: Median age = 68 (range 46 - 81); men = 55%; median ECOG PS = 1 (11% - 0, 61% - 1, 28% - 2); median KPS = 80 (5% - 100, 28% - 90, 47% - 80, 12% - 70, 8% - 60). Correlation of the two PS methods was fairly high at 0.75 (Pearson r). While this was a positive result, of concern was the finding that the 21 patients with ECOG PS = 2 were as likely to be rated as having a KPS of 60 or 70 or 80, indicating a broad range of activity encompassed by the ECOG 2 category. In contrast, of those with KPS < 80, only 2 patients (3%) had ECOG PS ≤ 1. Sensitivity/specificity results for KPS are 0.86/0.87; for ECOG are 0.96/0.62. Conclusions: Both methods have a high degree of sensitivity; however, the greater specificity of the KPS indicates that it is a better choice 1) for decision making for suitability for chemotherapy (fewer inappropriate patients treated), and 2) for more accurately identifying which patients would benefit from having genomic studies performed to help determine individualized chemotherapy selection. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Aventis Aventis Aventis Aventis Aventis Aventis

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)0.002
metaresearch head score (Gemma)0.004
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 score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.049
GPT teacher head0.470
Teacher spread0.422 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

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