The Clinical Staging Model in Psychosis: a Cross-Disciplinary Critical Appraisal Informed by Oncology
Bibliographic record
Abstract
Categorical models of mental illness have come under increasing scrutiny in recent years, but there is no uniformly accepted alternative. One option that has gained attention in psychiatry is clinical staging, which frequently refers to staging models in other areas of healthcare, most prominently that of oncology/cancer. Yet, such comparisons are often inadvertently broad and leave unanswered questions about the applicability of cancer staging models to mental health. To address this gap, we convene expertise in oncology and psychiatry to better understand features of the clinical staging model in theory and practice as applied to cancer, and its potential translation to psychosis as an example of a mental illness. We compare and contrast features of the staging model in the context of illness development in cancer, consider how these features might port over to psychosis, and finally, the ways in which staging might need to be adapted if it is to have validity and clinical utility for psychosis.
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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 | 0.226 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".