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Record W2789246254 · doi:10.1177/0825859718755249

Correlation of Palliative Performance Scale and Survival in Patients With Cancer Receiving Home-Based Palliative Care

2018· article· en· W2789246254 on OpenAlexaff
Jiaoli Cai, Denise N. Guerriere, Hongzhong Zhao, Peter C. Coyte

Bibliographic record

VenueJournal of Palliative Care · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPalliative careMedicineProportional hazards modelConfidence intervalHazard ratioProspective cohort studyMultivariate analysisSurvival analysisReceiptInternal medicineNursing

Abstract

fetched live from OpenAlex

The main objective of this study was to examine whether and how the Palliative Performance Scale (PPS), a measure of a patient's function, was predictive of survival time for those in receipt of home-based palliative care. This was a prospective study, which included 194 cancer patients from November 17, 2013, to August 18, 2015. Data were collected from biweekly telephone interviews with caregivers. Kaplan-Meier survival curves were estimated to assess how survival time was correlated with initial PPS scores after admission to the home-based palliative care program. A multivariate extended Cox regression model was used to examine the association between PPS and survival. The results showed that patients with higher PPS scores, that is, better function, had a lower hazard ratio (0.977; 95% confidence interval: 0.965-0.989) and hence longer survival times. The PPS can be used in predicting survival time for home-based palliative care patients.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.360
Teacher spread0.312 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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