Impacto de las nuevas tecnologías en la neurología en España. Revisión del Comité Ad-Hoc de Nuevas Tecnologías de la Sociedad Española de Neurología
Bibliographic record
Abstract
Las nuevas tecnologías (NT) están cada vez más presentes en el ámbito biomédico. Utilizando la definición de consenso de NT del Comité Ad-Hoc de Nuevas Tecnologías de la Sociedad Española de Neurología (SEN), se evalúa su impacto en la neurología española a través de las comunicaciones de las reuniones anuales de la SEN. Se define el concepto de NT en neurología como una tecnología novedosa o aplicación de una tecnología anterior, caracterizada por un cierto grado de coherencia persistente en el tiempo, con potencial de tener impacto en el presente y futuro de la neurología. Se plantea un estudio descriptivo tomando como fuente las comunicaciones de las reuniones de la SEN desde 2012 hasta 2018 y analizando los tipos de NT empleadas, la subespecialidad, así como su distribución territorial. De las 8.139 comunicaciones presentadas, 299 estaban relacionadas con NT (3,7%), incluyendo 120 pósteres y 179 comunicaciones orales, variando desde el 1,6% en 2012 hasta el 6,8% en 2018. Los tipos de tecnología mayormente representados fueron neuroimagen avanzada (24,7%), biosensores (17,1%), electrofisiología y neuroestimulación (14,7%) y telemedicina (13,7%). Las áreas neurológicas con mayor empleo de NT fueron trastornos del movimiento (18,4%), enfermedades cerebrovasculares (15,7%) y demencias (13,4%). Madrid fue la comunidad que presentó más comunicaciones (32,8%), seguida por Cataluña (26,8%) y Andalucía (9,0%). Las comunicaciones sobre NT siguen una tendencia creciente. El número de NT empleadas ha ido aumentando de manera paralela a la disponibilidad tecnológica. Se encontraron comunicaciones en todas las subespecialidades neurológicas, con una distribución geográfica heterogénea. New technologies (NT) are increasingly widespread in biomedicine. Using the consensus definition of NT established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN. We defined the concept of NT in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of NT, the field of neurology, and the geographical provenance of the studies. We identified 299 communications related with NT from a total of 8,139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where NT were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to NT (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%). The number of communications addressing NT follows an upward trend. The number of NT used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".