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Record W6887850156 · doi:10.17632/v2g4p7h4fd

Intervenciones no-farmacológicas para efectos priorizados por pacientes, de quimioterapia antineoplásica: revisión sistemática

2023· dataset· es· W6887850156 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2023
Typedataset
Languagees
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Context (archaeology)

Abstract

fetched live from OpenAlex

Diferentes intervenciones no farmacológicas se han estudiado para manejar los síntomas derivados de la quimioterapia. Se realizo una revisión sistemática de la literatura con el objetivo de describir las intervenciones no farmacológicas para el manejo de síntomas secundarios a la quimioterapia antineoplásica en adultos. Se revisaron estudios experimentales y observacionales analíticos (2021 a 2023). La selección de estudios y extracción de datos se realizó de forma paralela. Las discrepancias se resolvieron con un tercer revisor. Se evaluó el riesgo de sesgo con las herramientas Risk Of Bias (RoB) y The Newcastle-Ottawa Scale (NOS). La síntesis de la literatura se realizó de forma descriptiva por desenlace priorizado. En esta revisión los desenlaces priorizados por pacientes y profesionales en el área de oncología fueron neutropenia, dolor, neuropatía, náuseas, vomito, alopecia, anorexia y desordenes del sueño. Se encontraron 7520 referencias, 62 incluidas para el análisis. La acupresión mostró un posible efecto en el control de síntomas como las náuseas y vomito. La intervención con frio en el cuero cabelludo mostro diferencias en los estadios de la severidad de alopecia. Las otras intervenciones mostraron heterogeneidad.

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)0.010
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0160.016
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.254

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 head0.103
GPT teacher head0.375
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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Same venueMendeley DataFrench-language works237,207