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Integrating palliative care into care of patients with kidney cancer and melanoma.

2014· article· en· W2526268649 on OpenAlexaboutno aff
Mary K. Buss, Susan DeSanto‐Madeya, Jessica Lynch, Jessica A. Zerillo, David F. McDermott

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalliative careReferralPsychosocialQuality of life (healthcare)Advance care planningCancerFamily medicineInternal medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

56 Background: Early palliative care (PC) has been shown to improve quality of life (QOL), enhance quality end-of-life (EOL) care, and reduce costs, yet many cancer centers lack resources to provide outpatient palliative care services. Data is needed to identify the optimal timing and most effective and efficient model for integrating PC into the care of cancer patients. In order to understand what components of PC have the greatest impact on patients, we examined the acceptability of early PC among patients with kidney cancer (RCC) and melanoma (M) and describe the content of PC visits. Methods: In July 2013, the outpatient PC team at Beth Israel Deaconess Medical Center, including physician (MD), social worker (SW) and chaplain (chap) were invited to see patients within a clinic that specializes in seeing patients with advanced RCC and M. All patients completed data on symptom burden using the Edmonton Symptom Assessment Scale, measures of QOL and degree of psycho-social and spiritual support. Referral to PC was based on these metrics and physician discretion. Results: Of the 21 patients seen by PC, 57% had RCC; 76% male; 86% White; 57% married. Mean age was 62 (range: 36-87). At the 1st PC visit: All had locally adv/metastatic disease; 48% were being treated with curative intent; 29% had not yet started treatment; 57% rated pain 0 on 0-10 scale (range: 0-9). First PC visit content: 76% psychosocial support (including building rapport); 67% symptom management; 33% included advance care planning (ACP) and decision-making support. Ninety-one percent were seen >1 by PC; 19% seen by SW or chap; median PC visits: 3. Eighty-one percent completed health care proxy. Additional data on patient outcomes will be presented. Conclusions: Nearly half of patients seen by PC were being treated with curative intent, suggesting acceptability of early PC integration in patients with RCC and M. Symptom management and psychosocial support dominated the early PC visits.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.158
GPT teacher head0.526
Teacher spread0.368 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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