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Record W2480923370 · doi:10.1016/j.rca.2016.05.006

Adaptación colombiana de las guías de neuroestimulación espinal en el manejo del dolor crónico e isquémico

2016· article· es· W2480923370 on OpenAlexaff
Juan Miguel Griego, María Patricia Gómez, Omar Fernando Gomezese, Adriana Margarita Cadavid, Carlos Jaime Yepes, Tatiana Mayungo, Jorge Acosta‐Reyes, Héctor Julio Meléndez, José Julián López, Luis Enrique Chaparro, L. Cifuentes

Bibliographic record

VenueColombian Journal of Anesthesiology · 2016
Typearticle
Languagees
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineHumanitiesGynecology

Abstract

fetched live from OpenAlex

El manejo del dolor crónico por medios convencionales a menudo es insuficiente, y es por eso que con el mayor conocimiento de la neurobiología del dolor se han desarrollado nuevos tratamientos, como la neuroestimulación espinal, con resultados óptimos a corto y a largo plazo. Integrar y actualizar guías de práctica clínica sobre la efectividad y la seguridad de la neuroestimulación espinal en el manejo del dolor crónico. Se realizó una búsqueda de guías de práctica, revisiones sistemáticas y ensayos clínicos en las principales bases de datos (Cochrane, EMBASE, LILACS y MEDLINE) evaluando su calidad y el grado de evidencia para proponer recomendaciones en el manejo de síndromes dolorosos crónicos, y en isquemia cardiaca y de miembros inferiores. Se encontró evidencia suficiente para soportar el uso de la neuroestimulación espinal para el alivio del dolor que persiste después de cirugía de espalda y también para el síndrome doloroso regional complejo. Se encontró evidencia en ascenso para el uso en la angina de pecho refractaria y en la extremidad inferior isquémica dolorosa. La neuroestimulación es una técnica mínimamente invasiva útil para el manejo de dolor persistente posterior a cirugía de columna y para el síndrome regional complejo. Management of chronic pain by conventional means is usually insufficient, but the enhanced knowledge of the neurobiology of pain has led to the development of new treatments like spinal neurostimulation, with optimal short and long-term results in the hands of the treating physicians. To integrate and update clinical practice guidelines on the effectiveness and safety of spinal neurostimulation in the management of chronic pain. Search of practice guidelines, systematic reviews and clinical trials in the main databases (Cochrane, EMBASE, LILACS and MEDLINE), and assessment of their quality and level of evidence in order to propose recommendations for the management of chronic painful syndromes and cardiac and lower-limb ischemia. Sufficient evidence was found to support the use of spinal neurostimulation for pain relief in cases of persistent pain after back surgery and also for complex regional pain syndrome. Growing evidence was found for the use of spinal neurostimulation in refractory angina pectoris and in painful ischemic lower limbs. Neurostimulation is a minimally invasive technique useful for the management of persistent pain after back surgery and for complex regional pain syndrome.

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)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.006
Science and technology studies0.0010.003
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.016
GPT teacher head0.296
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2016
Admission routes1
Has abstractyes

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