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Record W3009215968 · doi:10.3389/fpsyt.2020.00135

Crossing Death Valley: Bringing Neurotechnology to Psychiatric Clinics in Alberta, Canada

2020· article· en· W3009215968 on OpenAlexafffundabout
Frank P. MacMaster, Nick Mitchell, Allison Bichel, Marni Bercov, Gayle Thompson, Victoria Y. M. Suen, Allison Strilchuk, Katherine Rittenbach

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsAlberta Health ServicesUniversity of AlbertaAlberta HealthUniversity of Calgary
FundersAlberta Health Services
KeywordsPsychiatryPsychologyMedicine

Abstract

fetched live from OpenAlex

Depression is a major public health problem, with a lifetime and 12-month prevalence estimated at 18 and 6% of adults. Depression is costly in terms of treatment and lost productivity and is the main burden of mental illness across the globe. Existing pharmacological and psychological treatments for depression result in clinically meaningful improvements in <60% of patients. An emerging treatment approach is non-invasive brain stimulation of depression-related brain targets through transcranial magnetic stimulation (TMS). In this perspective, we detail our efforts on bringing TMS to clinical populations in Alberta by utilizing a novel organizational structure that bridges the gap between academia and the health care system. The Addictions and Mental Health Strategic Clinical Network worked with stakeholders to (1) examine the evidence, (2) develop clinical tools for patient selection and protocol application, (3) create overall implementation and evaluation plans to aid in further scale and spread, and even (4) fund the purchase and deployment of devices. Through this work, five publicly supported clinics now exist in Alberta.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes3
Has abstractyes

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