Correction: Clinical utility of combinatorial pharmacogenomic testing in depression: A Canadian patient- and rater-blinded, randomized, controlled trial
Bibliographic record
Abstract
The original version of this article unfortunately contained mistakes. To ensure the accuracy of their article the authors would like to make the following corrections: Correction #1 Page 5, paragraph 1, line 5: Text currently reads “136”. This is incorrect and the authors are requesting to change it to “149”. Correction #2 Page 7, paragraph 4, line 17: Text currently reads “mild and moderately depressed patients”. This is incorrect and the authors are requesting to change it to: “patients with no or mild depression”. Correction #3 Supplementary Materials, Supplementary Table 2: The following Antipsychotic medications are incorrectly listed under Antidepressants: Olanzapine, Paliperidone, Perphenazine, Quetiapine, Risperidone, Thiothixene, Ziprasidone. The authors are requesting to update Supplementary Table 2 to include the above Antipsychotic medications in the Antipsychotic medication section of the table. The authors apologize for the errors. The original article has been corrected accordingly.
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 | 0.018 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".