MétaCan
Menu
Back to cohort
Record W7015866289

Validación de un instrumento de valoración del dolor severo en pacientes escolares postoperados del Servicio de Ortopedia y Traumatología del INSN

2018· dissertation· es· W7015866289 on OpenAlexaboutno aff

Bibliographic record

VenueUniversidad Peruana Cayetano Heredia Institutional Repository · 2018
Typedissertation
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInitial trainingOccupational trainingScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

El Objetivo del estudio será validar un instrumento de valoración del dolor severo en pacientes escolares postoperados del servicio de Ortopedia y Traumatología del INSN. Material y Método: Es de tipo Cuantitativo, observacional, de corte transversal. El estudio se realizará en el Servicio de Ortopedia y Traumatología del INSN - Breña. La Población será 30 de enfermeras del servicio de hospitalización de Ortopedia y Traumatología. Técnica e instrumento: será la observación sistemática y el instrumento sobre la valoración del dolor severo en pacientes escolares post operados será elaborado por las investigadoras tomando una parte del cuestionario del dolor de McGill y la escala de medición del dolor escala numérica de Walco y Howite y escala visual de Oucher. La validez del instrumento será por juicio de expertos, cada uno evaluará la validez de contenido, constructo y criterio utilizando la prueba binomial menor de 0.05 y para la confiabilidad se realizará una prueba piloto; se aplicará la confiabilidad interobservador con el índice kappa. El procedimiento de recolección de datos será aplicado por la enfermera a cargo de los pacientes y la toma de datos se realizará en los turnos laborales diurnos y nocturnos en un trimestre. Los datos se procesarán en Microsoft Excel 2016.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.006
GPT teacher head0.246
Teacher spread0.240 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueUniversidad Peruana Cayetano Heredia Institutional RepositorySame topicCell Image Analysis TechniquesFrench-language works237,207