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Record W3157941850 · doi:10.5205/8857

Pain pattern in patients with leg ulcers

2017· article· en· W3157941850 on OpenAlexaboutno aff
Isabelle Andrade Silveira, Beatriz Guitton Renaud Baptista de Oliveira, Aretha Pereira de Oliveira, Nelson Carvalho Andrade

Bibliographic record

VenueJournal of Nursing Ufpe Online · 2017
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicinePsychologyArt

Abstract

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RESUMO Objetivo : avaliar o padrao da dor de pacientes com ulceras de perna. Metodo : estudo exploratorio, descritivo, transversal, de abordagem quantitativa, realizado em um hospital universitario com amostra de 40 pacientes. A coleta de dados foi realizada por um instrumento composto pela identificacao do paciente, caracteristicas da ferida e avaliacao da dor (questionario de McGill e Escala Numerica da Dor). Os dados foram organizados por meio de digitacao em planilha eletronica do aplicativo Microsoft Excel, analisados pela estatistica descritiva e dispostos em tabelas. Resultados : a dor foi caracterizada como fisgada, pontada, latejante, agulhada, enjoada e queimacao, com padrao de intensidade moderada a forte. Aparece ao anoitecer, piora em posicao ortostatica, necessita de medicacao e elevacao dos membros para controle. Conclusao : as palavras escolhidas demonstraram que a dor tem carater nociceptivo e neuropatico. Ressalta-se a importância de avaliar a dor multidimensionalmente a fim de orientar as intervencoes de enfermagem visando ao controle efetivo da dor. Descritores : Ulcera da Perna; Dor; Enfermagem. ABSTRACT Objective : to evaluate the pain pattern of patients with leg ulcers. Method : it is an exploratory, descriptive, cross-sectional study with a quantitative approach performed in a university hospital with a sample of 40 patients. Data collection was performed by an instrument composed of patient identification, wound characteristics and pain evaluation (McGill questionnaire and Numerical Pain Scale). The data were organized by spreadsheets in the Microsoft Excel application, analyzed by descriptive statistics, organized in tables. Results : the pain was characterized as pinched, pricked, throbbing, needled, nauseous and burning, with a moderate to strong intensity pattern. It appears at dusk, worsens in orthostatic position, requiring medication and elevation of limbs for control. Conclusion : the chosen words demonstrated that the pain has nociceptive and neuropathic character. It is important to evaluate the pain multidimensionally to guide the nursing interventions aiming at the effective control of pain. Descriptors : Leg Ulcer; Pain; Nursing. RESUMEN Objetivo : evaluar el estandar del dolor en pacientes con ulceras de pierna . Metodo : estudio exploratorio , descriptivo, transversal, de enfoque cuantitativo, realizado en un hospital universitario con muestra de 40 pacientes. La recoleccion de datos fue realizada por un instrumento compuesto por la identificacion del paciente, caracteristicas de la herida y evaluacion del dolor (cuestionario de McGill y Escala Numerica del Dolor). Los datos fueron organizados por medio de digitacion en planilla electronica del aplicativo Microsoft Excel, analizados por la estadistica descriptiva, dispuestas en tablas. Resultados : el dolor se caracterizo como punzada, punzada, palpitante, pinchazo, nauseas y ardor con un patron de fuerte a moderada intensidad. Aparece al anochecer, empeora en posicion ortostatica, necesita de medicacion y elevacion de los miembros para control. Conclusion : las palabras escogidas demostraron que el dolor tiene caracter nociceptivo y neuropatico. Se resalta la importancia de evaluar el dolor multi-dimensionalmente para orientar las intervenciones de enfermeria visando el control efectivo del dolor. Descriptores : Ulcera de la Pierna; Dolor; Enfermeria. Normal 0 21 false false false PT-BR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Tabela normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi;}

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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 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".

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Citations2
Published2017
Admission routes1
Has abstractyes

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