MétaCan
Menu
Back to cohort
Record W2233591955 · doi:10.1377/hlthaff.2015.0752

Palliative Care Teams’ Cost-Saving Effect Is Larger For Cancer Patients With Higher Numbers Of Comorbidities

2016· article· en· W2233591955 on OpenAlexaff
Peter May, Melissa M. Garrido, J. Brian Cassel, Amy S. Kelley, Diane E. Meier, Charles Normand, Lee Stefanis, Thomas J. Smith, R. Sean Morrison

Bibliographic record

VenueHealth Affairs · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsTrinity College
FundersNational Cancer InstituteNational Institute on AgingU.S. Department of Veterans Affairs
KeywordsMedicinePalliative careReceiptComorbidityWorkforceHealth careEconomic shortageFamily medicineEmergency medicineNursingBusinessPsychiatry

Abstract

fetched live from OpenAlex

Patients with multiple serious conditions account for a high proportion of health care spending. Such spending is projected to continue to grow substantially as a result of increased insurance eligibility, the ever-rising cost of care, the continued use of nonbeneficial high-intensity treatments at the end of life, and demographic changes. We evaluated the impact of palliative care consultation on hospital costs for adults with advanced cancer, excluding those with dementia. We found that compared to usual care, the receipt of a palliative care consultation within two days of admission was associated with 22 percent lower costs for patients with a comorbidity score of 2-3 and with 32 percent lower costs for those with a score of 4 or higher. Earlier consultation was also found to be systematically associated with a larger cost-saving effect for all subsamples defined by multimorbidity. Given ongoing workforce shortages, targeting early specialist palliative care to hospitalized patients with advanced cancer and higher numbers of serious concurrent conditions could improve care while complementing strategies to curb the growth of health spending.

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 score0.365
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.062
GPT teacher head0.414
Teacher spread0.351 · 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

Citations146
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueHealth AffairsSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207