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Record W7120652348

Dor total em pacientes oncológicos em cuidados paliativos: revisão de escopo

2025· dissertation· pt· W7120652348 on OpenAlexaboutno aff
Mayara Maria Silva da Cruz Alencar

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2025
Typedissertation
Languagept
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsPalliative careContext (archaeology)GuidelinePain medicineCancer painPancreatic cancerDiseasePain and suffering
DOInot available

Abstract

fetched live from OpenAlex

Cicely Saunders, known as the modern founder of the palliative care movement, developed her idea of "total pain." Based on this concept, she placed pain as a fundamental element in health research, as much as the disease itself, and argued that the experience of pain could fluctuate without hierarchies between the physical, mental, social, and spiritual aspects of the patient. People with cancer pain face functional incapacity associated with the disease itself and the pain inherent in it. However, the visible limitations and losses they face affect not only the physical dimension but also the psychological, social, and spiritual dimensions, as well as the work, economic, and even family spheres. The overall objective of this research is to understand, through the literature, how total pain is assessed in adult cancer patients in the context of palliative care. This is a scoping review conducted according to the parameters and recommendations listed by the Joanna Briggs Institute (JBI) and adjusted to the PRISMA Extension Guideline for Scoping Reviews. Based on the Population, Concept, and Context (PCC) strategy proposed by the JBI, where P - Malignant Neoplasms; C - Total Pain; and C - Palliative Care. Therefore, the guiding question of this research was: "How is total pain assessment performed in adult cancer patients undergoing palliative care?" The sources considered were MEDLINE/PubMed, Cochrane Library, LILACS, Web of Science, Scopus, Excerpta Medica dataBASE, and CINAHL, as well as gray literature from secondary sources such as Google Scholar, OpenGray, BDTD, and WHO, as well as ANCP, INCA, ESMO, and ASCO. The sample consisted of 13 publications for analysis. Of the 13 publications, one was from North America (Canada), five from South America (Brazil and Chile), five from Europe (Italy, Poland, the United Kingdom, Denmark, and Germany), and two from Asia (Singapore and India). The discussion was approached through qualitative analysis in thematic categories such as: Multidisciplinary approach to total pain in cancer patients undergoing palliative care; methods for assessing total pain; total pain and its physical, social, psychological, and spiritual dimensions. The conclusion is that a multidisciplinary team is the most effective strategy in the context of "total pain," although its assessment remains fragmented. Interdisciplinary research involving professionals from various health specialties is proposed to enrich the understanding of the phenomenon under study, as well as the development and validation of specific instruments.

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)0.008
metaresearch head score (Gemma)0.031
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 score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0120.014
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.275
Teacher spread0.254 · 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
Published2025
Admission routes1
Has abstractyes

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