Role of the Pharmacist in Managing Treatment-Resistant Depression: A Focus on Ketamine
Bibliographic record
Abstract
The impact of depression is well described in the literature, and it is most prominent in patients who have trialed multiple treatments. Treatment-resistant depression (TRD) is particularly debilitating, and it is associated with significant morbidity and mortality. Despite this, there seems to be therapeutic inertia in adopting novel therapies in current practice. Ketamine is an N-methyl-D-aspartate receptor antagonist and anesthetic agent which has recently been shown to be effective in the management of TRD when administered intravenously or intranasally. The treatments, however, are not easily accessible due to restrictions in prescribing and dispensing, high costs, and the slow uptake of evidence-based practice involving ketamine within the Canadian healthcare system. Given the limited treatment options for TRD, novel approaches should be considered and adopted into practice, and facilitated by a multi-disciplinary approach. Pharmacists play a critical role in ensuring access to quality care. This includes dissemination of evidence supporting pharmacological treatments and facilitating translation into current practice. Pharmacists are uniquely positioned to collaborate with prescribers and assess novel treatment options, such as ketamine, address modifiable barriers to treatment, and triage access to medications during transitions of care. Extending the reach of these novel psychiatric treatments in both tertiary and primary care settings creates an emerging role for pharmacists in the collaborative effort to better manage treatment-resistant depression.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".