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2015· letter· en· W300881614 on OpenAlexaff
Clement Hamani, Julie G. Pilitsis, Anand I. Rughani, Joshua M. Rosenow, Parag G. Patil, Konstantin V. Slavin, Aviva Abosch, Emad Eskandar, Laura Mitchell, Steven N. Kalkanis

Bibliographic record

VenueNeurosurgery · 2015
Typeletter
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal capsuleDeep brain stimulationNeuroimagingMedical physicsMagnetic resonance imagingNeuroscienceRadiologyPsychiatryPathologyPsychology

Abstract

fetched live from OpenAlex

We thank Dr Mavridis for his kind words and thoughtful comments on our recently published guidelines.1 The issue of discrepancy in potential coordinates for placement of electrodes into the nucleus accumbens (NA) during deep brain stimulation (DBS) surgery is indeed important. In our article, however, we did not analyze targeting strategies, because our guidelines were largely performed to appraise the level of evidence for conducting DBS in patients with obsessive-compulsive disorder. We agree with the author that further research is needed. In addition, we note that most surgeons also rely on the direct magnetic resonance imaging visualization of the anterior limb of the internal capsule and the NA for targeting. To date, it remains unclear whether targeting of specific regions of the NA is feasible and may help to improve clinical results. Such conclusions will only be possible with an increase in surgical expertise and the development of studies not only to characterize anatomic and radiological landmarks, but also to assess the placement of electrode contacts yielding optimal surgical results. In the future, better neuroimaging techniques and/or the combined use of neuroimaging and electrophysiology may help us establish the optimal site for DBS surgery in the NA/anterior limb of the internal capsule. Disclosures Dr Hamani is a consultant for St Jude Medical. Dr Pilitsis is a consultant for St. Jude, Boston Scientific, and Medtronic and has grant support from Boston Scientfic, St Jude Medical, Medtronic and NIH. Dr Rosenow is a consultant for Boston Scientific Neuromodulation. Dr Patil is a consultant, advisory board member and/or received research grants from Medtronic, St. Jude Medical, Boston Scientific, and Monteris. Dr Abosch has an ad hoc consulting agreement with Medtronic. Dr Slavin is a consultant, advisory board member and/or received honoraria from Medtronic, St. Jude Medical, Boston Scientific, Bioness, Greatbatch, Stimwave and Nevro. The other authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.

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)0.006
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.947
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.092
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0180.027
Insufficient payload (model declined to judge)0.0530.040

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 head0.048
GPT teacher head0.282
Teacher spread0.234 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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