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Record W7120579484

Instruments for evaluating hospitalised patients in palliative care: integrative review

2022· dissertation· pt· W7120579484 on OpenAlexaboutno aff
Tárcilla Pinto Passos Bezerra

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2022
Typedissertation
Languagept
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPalliative careHealth professionalsMEDLINEScale (ratio)Advance care planningHealth care
DOInot available

Abstract

fetched live from OpenAlex

The need for careful assessment of patients as to their needs in palliative care motivated the present study, in which the objective was to identify instruments used for the assessment of the hospitalised patient in palliative care. This is an integrative literature review study, carried out from October to November 2021, on online database platforms: U.S. National Library of Medicine, Latin American and Caribbean Literature on information on Health Sciences, Scientific Electronic Library Online and the Virtual Health Library. A total of 126 scientific articles were located, of which 10 were selected to compose the study sample. Fifteen instruments were identified, six generic, four specific for people in palliative care, four specific for cancer patients and one for patients diagnosed with COVID-19. The instruments that were most repeated among the studies were: Palliative Performance Scale and Edmonton Symptom Assessment. The most relevant aspects to be evaluated in palliative care patients were: functional capacity, physical and psychological symptoms and advanced age. The instruments proved useful to guide health professionals in patient assessment, care planning and decision making.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0190.014
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.373
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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