Ejercicio terapéutico y estrategias de educación y auto-manejo para el tratamiento de los trastornos asociados al síndrome del latigazo cervical: Una revisión sistemática
Bibliographic record
Abstract
Objetivo: revisar el conocimiento cientifico disponible sobre si una estrategia combinando ejercicio terapeutico y/o tecnicas de educacion o auto-manejo es mas efectiva que cualquier otra intervencion terapeutica en el Sindrome de Latigazo Cervical. Material y metodo: se realizo una busqueda bibliografica en Pubmed, Web Of Science y PEDro con los siguientes criterios de inclusion: ensayos clinicos aleatorizados publicados entre 2012 y 2017, clasificando la patologia segun la Quebec Task Force y que al menos comparase una intervencion basada en ejercicio terapeutico y educacion o auto-manejo. Resultados: obtuvimos 3.587 resultados, de los cuales incluimos 13 en la sintesis narrativa. La calidad de los estudios incluidos vario entre media y alta. Estos estudios analizaron una serie de medidas de resultado que incluyeron: funcion cervical, discapacidad, dolor, funcion fisica cervical y global, variables psicologicas, adherencia y satisfaccion. Conclusiones: el ejercicio especifico cervical combinado con estrategias educativas y de auto-manejo mejoran las variables de resultado analizadas y parecen constituir una estrategia eficaz de tratamiento. Podrian ser usadas conjuntamente, desplazando estrategias unimodales y modalidades pasivas, que deberian emplearse como coadyuvantes y no como ejes terapeuticos. El ejercicio especifico cervical parece ser superior al ejercicio global y a la lista de espera, pero no podemos establecer la superioridad de un programa concreto.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".