Bibliographic record
Abstract
# Giving low dose tricyclics is not justified by evidence {#article-title-2} EDITOR—The meta-analysis by Furakawa et al must be considered downright naughty.1 While masquerading as a contribution to an academic debate about appropriate antidepressant treatment it actually does little more than endorse the widely held prejudice in favour of using low dose tricyclics in depression. Published in a widely read UK general medical journal, it will inevitably encourage a practice that is not encouraged by either the Royal College of Psychiatrists or the American Psychiatric Association. The subject of optimal dosage of tricyclics remains controversial, but the value of treatment with tricyclic antidepressants at standard dosage compared with placebo is abundantly clear. As Furakawa et al concede but perhaps do not emphasise sufficiently, the evidence for low dose tricyclic antidepressants is of generally poor quality. Many of the trials used in their analysis took place before standardised diagnostic or outcome criteria were commonplace. What is less clear is the motivation for their undertaking. Fluoxetine is now available in generic preparations for about £7 per month, significantly reducing the financial advantage of older antidepressants. Some evidence supports the use of tricyclics in severe depression, but presumably the authors would not recommend low dose tricyclics in such patients. The chronic severe nature of depression would make it unethical to recommend a treatment without a secure evidence base. Being charitable, one can only hope that the therapeutic advice was added to add colour to an otherwise unexceptional meta-analysis. 1. 1.↵1. Furukawa AT, 2. McGuire H, 3. Barbui C . Meta-analysis of effects and side-effects of low dosage tricyclics antidepressants in depression: systematic review. BMJ 2002; 325: 991–995 [OpenUrl][1][Abstract/FREE Full Text][2] # Evidence to change current guidelines is insufficient {#article-title-4} EDITOR—The study by Furukawa et al consists of two separate analyses looking at quite different things so it is difficult to draw any firm conclusions.1 The first meta-analysis of 35 … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.issn%253D0007-1447%26rft.aulast%253DFurukawa%26rft.auinit1%253DT.%2BA%26rft.volume%253D325%26rft.issue%253D7371%26rft.spage%253D991%26rft.epage%253D991%26rft.atitle%253DMeta-analysis%2Bof%2Beffects%2Band%2Bside%2Beffects%2Bof%2Blow%2Bdosage%2Btricyclic%2Bantidepressants%2Bin%2Bdepression%253A%2Bsystematic%2Breview%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.325.7371.991%26rft_id%253Dinfo%253Apmid%252F12411354%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=bmj&resid=325/7371/991&atom=%2Fbmj%2F326%2F7387%2F499.2.atom
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".