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Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial paper by Independent / Community
⭐ 65.4
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Paper

Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial

by Independent / Community 0004a22c06658aaa88124fcc8cbff97edc2501ad
Free2AITools Nexus Index
65.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 75
P: Popularity 50
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Background As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high...

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Semantic Scholar 9 Citations
Paper Information Summary
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Registry ID 0004a22c06658aaa88124fcc8cbff97edc2501ad
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{0004a22c06658aaa88124fcc8cbff97edc2501ad,
  author = {Unknown},
  title = {Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0004a22c06658aaa88124fcc8cbff97edc2501ad}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial [Paper]. Free2AITools. https://api.semanticscholar.org/0004a22c06658aaa88124fcc8cbff97edc2501ad

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 75
Popularity (P) 50
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial: Authority (A:75), Popularity (P:50), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Background As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high..."

❝ Cite Node 

@article{Unknown2026Impact,
  title={Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ9CitationsSemantic Scholar
πŸ›οΈ75AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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rag retrieval
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source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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citations
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