The Transdiagnostic Global Impression - Psychopathology scale (TGI-P): Initial development of a novel transdiagnostic tool for assessing, tracking, and visualising psychiatric symptom severity in everyday practice
Bibliographic record
Abstract
Lacking biomarkers in psychiatry calls for a valid and reliable assessment of psychopathology across mental disorders that is easy to use, bridges research and clinical care, and that can capture clinician and patient perspectives. Herein we propose, a novel, brief, transdiagnostic tool to assess and visualize symptom severity in different psychiatric disorders. The Transdiagnostic Global Impression - Psychopathology scale (TGI-P) is based on the Clinical Global Impression - Severity scale (CGI-S), which was originally designed to measure global illness severity in one score. The TGI-P covers 10 transdiagnostic symptom domains and similar to the CGI-S, it is rated on a 7-point Likert-scale from 1 (normal) to 7 (extreme). These ten domains include positive symptoms, negative symptoms, manic symptoms, depressive symptoms, addiction symptoms, cognitive symptoms, anxiety symptoms, sleep symptoms, hostility symptoms, and self-harm symptoms. The results are visually presented, thus simplifying the monitoring of symptoms, and facilitating discussion with patients and caregivers. As part of the development process, the TGI-P was surveyed among 36 psychiatrists from 3 countries. Importantly, over 80 % of them was "very positive" or "positive" about the concept of the tool, and most of them (70 %) reported willingness to use it in their everyday practice. Further psychometric development and testing of the TGI-P is underway alongside future TGI scales covering adverse events, functioning and satisfaction.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".