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The impact of early palliative care on the quality of life of patients with advanced pancreatic cancer: The IMPERATIVE study.

2020· article· en· W3005419411 on OpenAlexafffundabout
Stephanie Lelond, Harvey Max Chochinov, Paul Daeninck, Benjamin A. Goldenberg, Lisa M. Lix, Susan McClement, Christina Kim

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
FundersCancerCare Manitoba Foundation
KeywordsMedicineQuality of life (healthcare)Palliative carePancreatic cancerCancerAnxietyInternal medicineLung cancerDepression (economics)ConfoundingNursing

Abstract

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TPS777 Background: Pancreatic cancer is lethal. Chemotherapy can improve survival by months; however, many patients experience an overwhelming burden of cancer-associated symptoms and poor quality of life (QOL). Early palliative care (EPC) alongside standard oncologic care results in improved QOL and survival in patients with lung cancer. Although international guidelines recommend EPC for patients with advanced pancreatic cancer (PANC), the benefit is not known. Objectives: The primary objective is to test for change in QOL between baseline (BL) and 16 weeks (wk). Secondary objectives are to test for change between BL and 16 wk in (a) symptom control; and (b) depression and anxiety. Methods: This prospective case-crossover study of patients with PANC provides EPC plus standard oncologic care. Primary oncology clinics refer patients to an EPC team led by a palliative care physician and a clinical nurse specialist. BL questionnaires are completed prior to initial EPC assessment, then every 4 wk until wk 16. EPC visits are every 2 wk for the first month, every 4 wk until wk 16, and then as needed. QOL, symptom control, anxiety and depression are measured using the FACT-Hep tool, ESAS-r, HADS and PHQ-9, respectively. A generalized linear model will test for statistically significant change in scores between BL and 16 wk; chemotherapy (yes/no) is included as a confounding covariate; model fit will be assessed. A sample size of 20 patients provides 80% power after controlling for covariate effects. 40 patients will be enrolled to account for missing data. To date, 28 patients have enrolled and 17 have completed the intervention. Significance: The benefit of EPC for patients with PANC is not known, however, EPC is increasingly recognized internationally by patients and stakeholders as a critical intervention which may improve both QOL and satisfaction with care. The Canadian Partnership Against Cancer’s report on the patient experience states “the best possible patient experience means all people with cancer have equitable access to high-quality person-centered palliative care”. This study offers access to EPC and provides an environment in which the benefit of an integrated approach is evaluated.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.369
GPT teacher head0.594
Teacher spread0.226 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other 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

Citations1
Published2020
Admission routes3
Has abstractyes

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