On the Origins of MAOI Misconceptions: Reaffirming their Role in Melancholic Depression
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Bibliographic record
Abstract
The first monoamine oxidase inhibitors (MAOIs) used for the treatment of depression in the 1950–60s were credited with treating severe melancholic depression (MeD) successfully and greatly reducing the need for electroconvulsive therapy (ECT). Following the hiatus caused by the then ill-understood cheese reaction, MAOI use was relegated to atypical and treatment-resistant depressions only, based on data from insufficiently probing research studies suggesting their comparatively lesser effectiveness in MeD. The siren attraction of new ‘better’ drugs with different mechanisms amplified this trend. Following a re-evaluation of the data, we suggest that MAOIs are effective in MeD. Additionally, the broad unitary conceptualisation of major depressive disorder (MDD) in the DSM model diminished the chance of demonstrating distinctive responses to different antidepressant drugs (ADs) such as SSRIs, TCAs, and MAOIs, thereby further reducing the interest in MAOIs. More reliable categorical distinction of MeD, disentangling it from MDD, may be possible if more sensitive measuring instruments (CORE, SMPI) are used. We suggest these issues will benefit from re-appraisement via an inductive reasoning process within a binary (rather than a unitary) model for defining the different depressive disorders, allowing for the use of more reliable diagnostic criteria for MeD in particular. We conclude that MAOIs remain essential for, inter alia , TCA-resistant MeD, and should typically be used prior to ECT; additionally, they have a role in maintaining remission in cases treated with ECT (and ketamine/esketamine). We suggest that MAOIs should be utilized earlier in treatment algorithms and with greater regularity than is presently the case.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it