Executive summary: The author argues in a speculative but plausible way that psychiatric drug trials obscure real harms and benefits because they use linear symptom scales that compress long-tailed subjective intensities, causing averages to hide large individual improvements and large individual deteriorations.
Key points:
The author claims psychiatric symptoms have long-tailed intensity distributions where high ratings like “9” reflect states far more extreme than linear scales imply.
The author argues that clinical trials treat symptom changes arithmetically, so very steep increases in states like akathisia can be scored as equivalent to mild changes in other domains.
The author states that mixed valence creates misleading cancellations: improvements in shallow regions of one symptom can be outweighed by worsening in steep regions of another even if numerical scores net to zero.
The author suggests average effect sizes such as “0.3 standard deviations” can emerge from populations where a substantial minority gets much worse while others get modestly better.
The author claims that disorders like depression or psychosis and medications like SSRIs, antipsychotics, and benzodiazepines all show this pattern of steep-region side-effects being compressed by standard scales.
The author recommends mapping individual response patterns, tracking steep regions explicitly, and using criticality and complex-systems tools instead of linear aggregation when evaluating psychiatric drugs.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The author argues in a speculative but plausible way that psychiatric drug trials obscure real harms and benefits because they use linear symptom scales that compress long-tailed subjective intensities, causing averages to hide large individual improvements and large individual deteriorations.
Key points:
The author claims psychiatric symptoms have long-tailed intensity distributions where high ratings like “9” reflect states far more extreme than linear scales imply.
The author argues that clinical trials treat symptom changes arithmetically, so very steep increases in states like akathisia can be scored as equivalent to mild changes in other domains.
The author states that mixed valence creates misleading cancellations: improvements in shallow regions of one symptom can be outweighed by worsening in steep regions of another even if numerical scores net to zero.
The author suggests average effect sizes such as “0.3 standard deviations” can emerge from populations where a substantial minority gets much worse while others get modestly better.
The author claims that disorders like depression or psychosis and medications like SSRIs, antipsychotics, and benzodiazepines all show this pattern of steep-region side-effects being compressed by standard scales.
The author recommends mapping individual response patterns, tracking steep regions explicitly, and using criticality and complex-systems tools instead of linear aggregation when evaluating psychiatric drugs.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.