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Record W3037547440 · doi:10.1017/s1478951520000395

Associations between unmet palliative care needs and cognitive impairment in a sample of diverse, community-based older adults

2020· article· en· W3037547440 on OpenAlexaboutno aff
Elissa Kozlov, Matthew J. Wynn, M. Carrington Reid, Charles Henderson, XinQi Dong, Jo Anne Sirey

Bibliographic record

VenuePalliative & Supportive Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsMontreal Cognitive AssessmentCognitive impairmentGerontologyPalliative careCognitionMedicineNeeds assessmentActivities of daily livingPsychologyClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Given a large number of community-based older adults with mild cognitive impairment, it is essential to better understand the relationship between unmet palliative care (PC) needs and mild cognitive impairment in community-based samples. METHOD: Participants consisted of adults ages 60+ receiving services at senior centers located in New York City. The Montreal Cognitive Assessment (MoCA) and the Unmet Palliative Care Needs screening tool were used to assess participants' cognitive status and PC needs. RESULTS: Our results revealed a quadratic relationship between unmet PC needs and mild cognitive impairment, controlling for gender, living status, and age. Participants with either low or high MoCA scores reported lower PC needs than participants with average MoCA scores, mean difference of the contrast (low and high vs. middle) = 2.15, P = 0.08. SIGNIFICANCE OF RESULTS: This study is a first step toward elucidating the relationship between cognitive impairment and PC needs in a diverse community sample of older adults. More research is needed to better understand the unique PC needs of older adults with cognitive impairment living in the community.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.120
GPT teacher head0.398
Teacher spread0.278 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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