La extensión de la declaración PRISMA para revisiones sistemáticas que incorporan metaanálisis en red: PRISMA-NMA
Bibliographic record
Abstract
On-line el 31 de marzo de 2016 IntroducciónLas revisiones sistemáticas que incluyen metaanálisis de ensayos clínicos aleatorizados, cuando están bien dise ñadas y realizadas, pueden proporcionar la mejor evidencia científica sobre el efecto de las intervenciones sanitarias.Las revisiones sistemáticas con metaanálisis permiten estudiar la eficacia y la seguridad de un tratamiento respecto a otro con un elevado nivel de calidad y rigor científico para así ayudar en la toma de decisiones en la asistencia sanitara.Sin embargo, en ocasiones la presentación y la descripción de algunas revisiones sistemáticas y metaanálisis siguen sin ser del ଝ Nota: En este artículo especial se presenta la traducción oficial en espa ñol de la lista de comprobación de la extensión de la declaración PRISMA para revisiones sistemáticas que incorporan metaanálisis en red (PRISMA-NMA).Cita del artículo original en inglés: Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al.The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: Checklist and explanations.Ann Intern Med.
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 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 | 0.523 | 0.837 |
| Meta-epidemiology (narrow) | 0.009 | 0.011 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".