Effectiveness of dry needling for headache: A systematic review.

Introducción El uso de tratamientos no farmacológicos en pacientes con cefalea, como la punción seca (PS), está asociado a una baja morbimortalidad y a un bajo coste sanitario. Algunos han demostrado utilidad en la práctica clínica. El objetivo de esta revisión fue analizar el grado de evidencia de la efectividad de la PS en la cefalea. Métodos Revisión sistemática de ensayos clínicos aleatorizados sobre cefalea y PS en las bases de datos biomédicas PubMed, Web of Science, Scopus y PEDro. Se evaluó la calidad de los estudios incluidos mediante la escala PEDro por 2 evaluadores de forma independiente. Resultados De un total de 136 estudios, se seleccionaron 8 ensayos clínicos publicados entre 1994 y 2019, incluyendo en total 577 pacientes. Dos estudios evaluaron pacientes con cefalea cervicogénica, otros 2, pacientes con cefalea tensional, y otro, pacientes con migraña. Los otros 3 estudios evaluaron pacientes con cefalea de características mixtas (tensional/migraña). La calidad de los estudios incluidos osciló entre «baja» (3/10) y «alta» (8/10). La eficacia de la PS sobre los episodios de cefalea fue similar a la de los tratamientos con los que se comparó. No obstante, obtuvo mejoras significativas respecto a variables funcionales y de sensibilidad. Conclusiones La punción seca es una técnica a considerar para el tratamiento de las cefaleas en la consulta, pudiendo utilizarse de forma rutinaria, bien de forma aislada, bien en combinación con terapias farmacológicas. Introduction Non-pharmacological treatment of patients with headache, such as dry needling (DN), is associated with less morbidity and mortality and lower costs than pharmacological treatment. Some of these techniques are useful in clinical practice. The aim of this study was to review the level of evidence for DN in patients with headache. Methods We performed a systematic review of randomised clinical trials on headache and DN on the PubMed, Web of Science, Scopus, and PEDro databases. Methodological quality was evaluated with the Spanish version of the PEDro scale by 2 independent reviewers. Results Of a total of 136 studies, we selected 8 randomised clinical trials published between 1994 and 2019, including a total of 577 patients. Two studies evaluated patients with cervicogenic headache, 2 evaluated patients with tension-type headache, one study assessed patients with migraine, and the remaining 3 evaluated patients with mixed-type headache (tension-type headache/migraine). Quality ratings ranged from low (3/10) to high (7/10). The effectiveness of DN was similar to that of the other interventions. DN was associated with significant improvements in functional and sensory outcomes. Conclusions Dry needling should be considered for the treatment of headache, and may be applied either alone or in combination with pharmacological treatments

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives