The active monitoring of oxytocin research evidence (AMORE) platform
Bibliographic record
Abstract
Oxytocin, an evolutionarily conserved neuropeptide, plays a crucial role in various physiological and behavioural processes, offering potential therapeutic benefits for several psychiatric and neurodevelopmental conditions. Despite its promise, oxytocin research has been marked by inconsistent results concerning its therapeutic applications and underlying mechanisms. Performing a systematic review and meta-analysis is a popular approach to shed light on mixed findings in a body of literature; however, they can become quickly outdated as new evidence becomes available. Given these challenges, research on the links between oxytocin and biobehavioural outcomes is ideally positioned for the adoption of 'living' meta-analyses, which allow for the continuous integration of new data and updated conclusions. Here we introduce the Active Monitoring of Oxytocin Research Evidence (AMORE) platform (https://amore-project.org), which is a hub that aggregates articles and materials associated with living meta-analyses for biobehavioural oxytocin research in humans. Developed through consensus among 24 expert researchers, a standardized framework was established that either requires or recommends practices ensuring transparency and rigor in living meta-analyses featured on the AMORE platform. Overall, AMORE has been designed to advance human oxytocin biobehavioural research by the timely integration of emerging evidence through transparent living meta-analyses. To date, two living meta-analysis projects at different stages of publication are hosted on AMORE, demonstrating the platform's practical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".