Crossing Death Valley: Bringing Neurotechnology to Psychiatric Clinics in Alberta, Canada
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".