Bibliographic record
Abstract
Joanna Moncrieff, MBBS, MRCPysch, MSc, MD1 (Can J Psychiatry 2007;52:100-101) Psychiatrists have been trying to construct a biological theory of depression for decades. Numerous candidates have been proposed, from noradrenalin and serotonin abnormalities to cortisol excess, hippocampal insufficiency, and neurotrophic factor. In all cases, results are inconsistent, and where abnormalities are found, they have not been shown to be specific or causal. For example, contrary to Dr Ravindran and Dr Kennedy's suggestions, the evidence on hippocampal volume is weak. Numerous studies show no difference between subjects with depression and control subjects, and I could not find studies supporting their assertion that duration of untreated illness correlates with volume reduction. In contrast, studies show that duration of treated illness predicts volume reduction.1,2 This raises the possibility that drug treatments for depression reduce brain volume in the same fashion as antipsychotics have been shown to do in patients with psychosis.3 In addition, loss of hippocampal volume is also found in first-episode psychosis4 and posttraumatic stress disorder5 and was recently shown in women diagnosed with dissociative identity disorder.6 Metabolic abnormalities sometimes found in depression are also not specific. For example, acute psychosis is also associated with increased hypothalamopituitary-adrenal axis function.7 Even if biochemical or structural abnormalities were found to be associated with depression, this would not imply that they were causal. If I experience an adverse event, I will feel sad, and if this emotion is strong enough, there are likely to be associated biochemical changes-but it is the event that has made me sad, not the chemical fluctuations. They are best viewed as an accompaniment, or a biological correlation, of the emotional state. In my first piece, I concentrated on placebo-controlled studies because it is difficult to show any real-world benefits from the use of antidepressants. In fact, their increased use is associated with increasing prevalence and duration of depressive episodes. The naturalistic Sequenced Treatment Alternatives to Relieve Depression trial found remission rates that are unimpressive in a naturally remitting condition, although the fact that this study did not include a placebo group means this valuable opportunity to evaluate the effectiveness of antidepressants was wasted.8 The study on absence due to sickness quoted by Ravindran and Kennedy actually found that individuals treated with antidepressants were less likely to return to work than those who were not treated with them (P Findings that patients who comply with optimal or adequate dosages do better than those who do not probably reflect the fact that individuals who comply with any treatment, including placebo, have better outcomes than those who do not.10 The study of quality of life was a discontinuation study that demonstrated a deterioration in individuals who had improved on drug treatment and were then randomized to placebo. As such, it provides no information on the benefits of antidepressants when they are compared with prospectively started placebo treatment.11 We do indeed live in an age characterized by an epidemic of psychological disorders.12, p 40 However, the mass prescribing of antidepressants and the concomitant message that depression is a brain disease have helped to create this situation, not to improve it. By persuading people that their thoughts and feelings originate from a biological defect, we are preventing them from finding real solutions to the complex problems of modern living. References 1. MacQueen GM, Campbell S, McEwen BS, et al. Course of illness, hippocampal function, and hippocampal volume in major depression. Proc Natl Acad Sci U S A. 2003;100(3):1387-1392. …
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.023 | 0.041 |
| Insufficient payload (model declined to judge) | 0.039 | 0.051 |
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".