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Record W2408261721 · doi:10.1177/082585971403000205

Is Performance Status Associated with Symptom Scores?

2014· article· en· W2408261721 on OpenAlexafffundabout
Rinku Sutradhar, Clare Atzema, Hsien Seow, Craig C. Earle, Joan Porter, Doris Howell, Deborah Dudgeon, Lisa Barbera

Bibliographic record

VenueJournal of Palliative Care · 2014
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsQueen's UniversityCancer Care OntarioInstitute for Clinical Evaluative SciencesMcMaster UniversityUniversity of Toronto
FundersOntario Institute for Cancer ResearchCancer Research Institute
KeywordsPerformance statusMedicinePopulationAssociation (psychology)GerontologyCancerPhysical therapyPsychologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Symptom scores and performance status are both important measures for patients with cancer. However, since performance status is not often part of routinely collected data, there is interest in exploring whether it can be calculated from symptom scores. METHODS: This was a population-based longitudinal study of cancer outpatients in Ontario, Canada in the year following their cancer diagnosis and among the subset of patients during the last year of their lives. RESULTS: In the first year after diagnosis, there was a significant relationship between performance status and fatigue and appetite; fatigue and well-being had a significant association with performance status in the last year of life. In both periods, the associations, although statistically significant, were not large enough to be clinically meaningful. CONCLUSION: Performance status is an important measurement that cannot be substituted or captured with symptom scores; it is important for healthcare providers to record performance scores on a regular basis.

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 score0.009
Threshold uncertainty score0.374

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 studies0.0000.000
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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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

Citations10
Published2014
Admission routes3
Has abstractyes

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