MétaCan
Menu
Back to cohort
Record W4229064262 · doi:10.1097/nhh.0000000000001076

Assessment and Management Tools for Advancing Disease

2022· article· en· W4229064262 on OpenAlexaboutno aff

Bibliographic record

VenueHome Healthcare Now · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsPalliative careAdvance care planningInclusion (mineral)Health careAgency (philosophy)MEDLINEDiseaseDisease managementPopulation

Abstract

fetched live from OpenAlex

As the population of the United States has aged, the number of home care patients with multiple chronic diseases has also increased. A review of the literature suggested that layering palliative care principles on traditional home healthcare could improve outcomes for patients with advancing disease. The purpose of this quality improvement project was to educate home care staff on the use of assessment and management tools to identify symptoms and provide symptom control, prevent unnecessary hospitalizations, and reduce healthcare costs in patients with advancing disease. This project took place at eight offices of a national home care agency in Kentucky and Indiana. Nurses, social workers, and therapists attended mandatory education sessions over 5 months on integrating palliative care principles into care planning for home care patients and using the Edmonton Symptom Assessment Scale (ESAS) and state-specific end-of-life planning tools. Symptoms were tracked and managed. A paired-samples t-test was used to analyze ESAS scores. Hospitalization and rehospitalization rates and costs were compared and benchmarked. Some symptoms worsened from beginning to end of the episode of care, suggesting a need for additional staff training about symptom management. The wide range of symptoms and symptom worsening suggest the need for individualized care, possibly extended home healthcare, and the inclusion of a more formal palliative care course for staff on symptom management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.442
Teacher spread0.380 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueHome Healthcare NowSame topicGeriatric Care and Nursing HomesFrench-language works237,207