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Record W3000598385 · doi:10.1017/s147895151900107x

Instruments for pain assessment in patients with advanced dementia: A systematic review of the evidence for Latin America

2020· review· en· W3000598385 on OpenAlexaboutno aff
Silvia Mercedes Coca, Roberto Ariel Abeldaño Zúñiga

Bibliographic record

VenuePalliative & Supportive Care · 2020
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaPain assessmentPain scaleMedicineSystematic reviewVascular dementiaDementia with Lewy bodiesScale (ratio)MEDLINEDiseasePhysical therapyPain managementCartographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Pain treatment is an essential component of care for elderly patients with advanced dementia. The objective of this study was to identify and analyze the different scales used for pain assessment in elderly persons diagnosed with dementia, in the literature available at the Latin American level. METHOD: A systematic review was performed on the existing scales for pain assessment in elderly people diagnosed with Alzheimer's disease, vascular dementia, and dementia with Lewy bodies. RESULTS: 226 articles were retrieved from the PUBMED, BIREME, and Scielo databases, of which a total of 10 articles entered the systematic review. The instruments identified in these publications were PAINAD, Abbey, McGill, and PACSLAC, while the Colored Pain Scale, Faces Pain Scale, and VAS scales were used as the silver standard. In Spanish, the Abbey scale, and in Portuguese, the PACSLAC scale showed the best reliability and validity coefficients. SIGNIFICANCE OF RESULTS: It is concluded that there are only two appropriate scales for the assessment of pain in people with dementia in the region of interest of this study. It is recommended to generate more evidence for a more accurate assessment of pain in people with dementia.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.383
Teacher spread0.331 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations9
Published2020
Admission routes1
Has abstractyes

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