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Record W2588422719 · doi:10.1200/jop.2016.018390

Implementing a Method for Evaluating Patient-Reported Outcomes Associated With Oral Oncolytic Therapy

2017· article· en· W2588422719 on OpenAlexaboutno aff
Emily Mackler, Laura Petersen, Jane Alcyne Severson, Douglas W. Blayney, Lydia L. Benitez, Caitlin R. Early, Shannon Hough, Jennifer J. Griggs

Bibliographic record

VenueJournal of Oncology Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersAmgen
KeywordsMedicineOncolytic virusMEDLINEMedical physicsIntensive care medicineCancerInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The paradigm shift in health care toward value-based reimbursement has brought emphasis to providing better quality of care to patients with chronic diseases, including patients with cancer. In accordance with providing better quality of care to patients, there has been a growing interest in evaluating quality of life through patient-reported outcomes (PROs). The revised Edmonton Symptom Assessment Scale (ESAS-r) is a tool that can be used to assess PROs and has been validated for use in patients with cancer. This initiative sought to use this standard assessment tool to acquire PROs concerning symptom burden from patients prescribed oral oncolytics. PATIENTS AND METHODS: Eight oncology practices in the state of Michigan used a modified ESAS-r to evaluate symptom burden of patients prescribed oral oncolytics before each outpatient visit. Thirteen symptoms were categorized as mild (0 to 3), moderate (4 to 6), or severe (7 to 10). RESULTS: A total of 1,235 modified ESAS-r surveys were collected and analyzed; 82.5% of symptoms were categorized as mild, 11.9% of symptoms were categorized as moderate, and 5.6% of symptoms were categorized as severe. CONCLUSION: PROs can be evaluated through the use of a standardized tool, such as the ESAS-r, in oncology patients receiving oral oncolytic therapy. Implementing such a tool in both community and academic practices is feasible and may facilitate improvements in the quality of care.

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)0.007
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.158
GPT teacher head0.525
Teacher spread0.367 · 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 designOther design
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

Citations20
Published2017
Admission routes1
Has abstractyes

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