Early Clinical Experiences of Esketamine Nasal Spray in the UK in Adults with Treatment-Resistant Major Depressive Disorder: Advisory Panel Recommendations
Bibliographic record
Abstract
Purpose: Treatment-resistant depression (TRD) is associated with profound morbidity for patients, placing a significant burden on those affected, the health service and wider society. Despite this, TRD remains chronically underserved in terms of viable treatment options. To address this gap, an advisory panel of psychiatrists and clinical researchers with experience in managing TRD convened to develop best practice statements on the use of esketamine nasal spray, one of the first TRD treatments to be licensed in 30 years. Methods: During a virtual meeting held on 12th November 2020, the advisory panel shared their experiences of using esketamine nasal spray in their clinical practice. The meeting focused on developing and refining recommendations for setting up and running an efficient esketamine nasal spray clinic for patients living with TRD. At the conclusion of the meeting, agreement was reached on all recommendation statements. Results: In setting up an esketamine nasal spray clinic, it is important to consider the logistical requirements involved and put measures in place to ensure it runs as efficiently as possible. Educating patients about the treatment and maintaining their well-being is paramount for preventing discontinuation. Putting in place checklists can be a useful strategy for ensuring treatment appointments run smoothly and safely. Conclusion: Providing additional treatment options for the management of TRD, such as esketamine nasal spray, is likely to be key to improving the long-term outcomes of this underserved patient population.
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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 | 0.061 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.023 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".