Bibliographic record
Abstract
A recent meta-analysis has suggested a 5-HTR1A promoter variant may predict antidepressant response. The present review comments on the claims made in view of sensitivity issues and issues pertaining to genetic exposure. We also alert to errors in the original data that had been carried over. Specifically, primers meant to amplify the HTR1A gene aligned to the BDNF gene sequence. Alleles had been confounded owing to DNA strand ambiguities and demographic information proved inaccurate. In the light of these findings, adherence to PRISMA guidelines and use of the Newcastle-Ottawa Scale did not safeguard against bias. More after action reviews are encouraged to identify factors likely to interfere with estimates of genetic risk in large data sets. These may result from pooling of ethnic groups, the use of binary data or other formats that are not human-readable, the introduction of surrogate identifiers and a failure to reverse-engineer previously published experimental protocols. Unless the above challenges are met, sequence variants are unlikely to inform personalized medicine strategies in psychiatry.
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 | 0.046 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.034 | 0.034 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".