Bibliographic record
Abstract
Can a 'true' effect be built on a 'wrong' model?Thase et al use a sophisticated model to assess the 'true' effect of active antidepressant therapy v. placebo. 1 Health authorities generally evaluate the efficacy of new medications from randomised controlled trials (RCTs) v. placebo which are well documented and rely on such a simple statistical paradigm that they can resist the major financial conflicts of interest inherent in the evaluation of pharmaceuticals.Concerning antidepressants, these studies generally identify small, average drug-placebo differences.2 Using statistical modelling, other authors have addressed the question of outcome measurement 3 and found that efficacy is better understood as a large effect in a subgroup of patients.This is consistent with the common clinical viewpoint.However, Thase et al's model leads to a curious phenomenon: everything happens as if some patients were considered as non-benefiters, whereas their final score is markedly less than the score for patients considered as benefiters.As they state, 'Essentially, all models are wrong, but some are useful'.Can a 'true' effect of active antidepressant v. placebo be built on such a 'wrong' model?Surely not for a health authority.Nevertheless, it could be useful for researchers and clinicians as it generates hypotheses on the manner in which antidepressants are different from placebo.In this view, it is necessary to go further and compare the characteristics of benefiters with non-benefiters with two additional perspectives:
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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.039 | 0.032 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".