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Record W3120441980 · doi:10.3389/fnhum.2020.588458

International Legal Approaches to Neurosurgery for Psychiatric Disorders

2021· review· en· W3120441980 on OpenAlexafffund
Jennifer A. Chandler, Laura Y. Cabrera, Paresh K. Doshi, Shirley Fecteau, Joseph J. Fins, Salvador Guinjoan, Clement Hamani, Karen Herrera-Ferrá, C. Michael Honey, Judy Illes, Brian H. Kopell, Nir Lipsman, Patrick J. McDonald, Helen S. Mayberg, Roland Nadler, Bart Nuttin, Albino J. Oliveira‐Maia, Cristian Rangel, Raphael Rego Borges Ribeiro, Arleen Salles, Hemmings Wu

Bibliographic record

VenueFrontiers in Human Neuroscience · 2021
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsBC Children's HospitalUniversity of TorontoNeuroDevNetUniversity of British ColumbiaUniversity of ManitobaSunnybrook Health Science CentreUniversité LavalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsScope (computer science)HarmonizationPsychosurgerySet (abstract data type)Order (exchange)Field (mathematics)NeurosurgeryPolitical scienceMedicineLawPsychologyPsychiatryBusinessComputer science

Abstract

fetched live from OpenAlex

Neurosurgery for psychiatric disorders (NPD), also sometimes referred to as psychosurgery, is rapidly evolving, with new techniques and indications being investigated actively. Many within the field have suggested that some form of guidelines or regulations are needed to help ensure that a promising field develops safely. Multiple countries have enacted specific laws regulating NPD. This article reviews NPD-specific laws drawn from North and South America, Asia and Europe, in order to identify the typical form and contents of these laws and to set the groundwork for the design of an optimal regulation for the field. Key challenges for this design that are revealed by the review are how to define the scope of the law (what should be regulated), what types of regulations are required (eligibility criteria, approval procedures, data collection, and oversight mechanisms), and how to approach international harmonization given the potential migration of researchers and patients.

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)0.005
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.005
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.002

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 head0.150
GPT teacher head0.349
Teacher spread0.199 · 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
GenreReview

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

Citations22
Published2021
Admission routes2
Has abstractyes

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Same venueFrontiers in Human NeuroscienceSame topicNeurological disorders and treatmentsFrench-language works237,207