Intranasal ketamine as a treatment for psychiatric complications of long COVID: A case report
Bibliographic record
Abstract
Background: Neuropsychiatric symptoms associated with long COVID are a growing concern. A proposed pathophysiology is increased inflammatory mediators. There is evidence that typical serotonergic antidepressants have limited efficacy in the presence of inflammation. Although ketamine has shown promise in MDD, there is limited evidence supporting the use of ketamine to treat depressive symptoms associated with long COVID. Case Report: This case took place on an inpatient psychiatry unit in a Canadian hospital. The patient was admitted with a 10-month history of worsening depression and suicidality following infection with COVID-19. Depressive symptoms and suicidal ideation were assessed throughout treatment using the Montgomery-Asberg Depression Rating Scale (MADRS). Written informed consent was obtained prior to data collection. This patient received 4 doses of intranasal ketamine which resulted in rapid improvement of depressive symptoms and complete resolution of suicidality with no major adverse events. Discussion: There is evidence to support long COVID symptoms result from dysregulated inflammatory processes. The presence of inflammation in patients with MDD has correlated to poor outcomes with first-line antidepressants. It has been demonstrated that IV ketamine is associated with decreased inflammatory mediators and proportional decrease in depressive symptoms. Conclusions: Intranasal ketamine in this case was effective at treating depressive symptoms and suicidal ideation associated with long COVID. This is consistent with available data that demonstrates ketamine's efficacy in reducing inflammatory mediators associated with neuropsychiatric symptoms. Therefore, ketamine may be a potential therapeutic option to treat long COVID and persistent depressive symptoms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".