Guidelines for the standardized collection of blood-based biomarkers in psychiatry: Steps for laboratory validity – a consensus of the Biomarkers Task Force from the WFSBP
Bibliographic record
Abstract
Recently, there has been a major shift in the field of psychiatry towards the exploration of complex relationships between blood-based biomarkers and the pathophysiology of psychiatric and neuropsychiatric disorders. However, issues with study reproducibility, validity and reliability have hindered progress towards the identification of clinically relevant biomarkers for psychiatry. The achievement of laboratory validity is a crucial first step for the posterior development of clinical validity. There is evidence that the variability observed in blood-based research studies may be minimised with the implementation of standardised pre-analytical methods and uniform clinical protocols (i.e., pre-venipuncture). It has been documented that errors made in the pre-analytical phase account for 46-68.2% of laboratory testing errors. Thus, standardising clinical assessment, ethical procedures and pre-analytical phase of clinical research is essential for the reproducibility, validity and reliability of blood marker assessment, and reducing the risk of invalid test results. Various other areas of research have already moved towards guidelines for the standardised collection of blood-based biomarkers. Here we aim to provide a set of guidelines that we believe would improve biomarker research: (1) pre-venipuncture information and documentation, (2) ethics of participant consent and (3) pre-analytical methods. Ultimately, we hope this will assist study planning and will improve data comparison across studies allowing for the discovery of biomarkers in psychiatry with both laboratorial and clinical validity.
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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 | 0.337 | 0.277 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.019 | 0.011 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".