The SENSOR System: Using Standardized Data Entry and Dashboards for Review of Scientific Studies Using the Clinical Applications of Psychedelics as an Illustration
Bibliographic record
Abstract
Abstract Introduction Literature reviews are useful tools for communicating the breadth of scientific discovery for a given topic. Irrespective of the nature of the review, data should be communicated in effective, easy to understand ways. In trying to address these limitations of traditional scientific reviews, we propose using dynamic data driven displays that have been used in multiple other industries to improve communication and decision making. Given the recent interest in the clinical applications of psychedelics for various mental health issues, we chose to test the SENSOR System (Standardized Data Entry and Dashboards for Review of Scientific Studies) as an alternative for an existing review article. Methods To validate the SENSOR System, an existing review with a topical, heterogenous, and growing set of studies was selected. In this case we chose the Wheeler et al. review on psychedelics in clinical practice where articles had already been preselected and reviewed. Detailed discussion of this review and the cited papers preceded designing the content and shared links for a Google Form for data intake, Google Drive for article access, and Google Sheets linked to the form intake data. Results A total of 46 study entries were made by 2 team members, including 3 articles published since the review to demonstrate the ease of updating the system Various representations of the Google Forms intake data in the SENSOR System dashboard are presented. Discussion Visual representation of review studies using a dashboard proved feasible and advantageous for numerous reasons. As the technology and guidelines for these systems evolve there is an opportunity to standardize reporting, centralize legacy datasets, streamline the submission process, improve collaboration between researchers, measure relative contribution of participating authors, and improve patient involvement. For the use case of clinical applications of psychedelics, limitations of conveying data accurately includes heterogeneity of study design, dosing, indications, and outcome measures. Conclusion Creation of a system for standardized data entry and dashboards for reviews of scientific studies is a feasible alternative and/or adjunct to the dissemination of summaries through traditional scientific review. There are numerous proposed advantages of the flexible, dynamic, and graphical display that requires further validation.
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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 | 0.059 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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".