State of the Science: The Hierarchical Taxonomy of Psychopathology (HiTOP)
Bibliographic record
Abstract
The Hierarchical Taxonomy of Psychopathology (HiTOP) is a dimensional framework for psychopathology advanced by a consortium of nosologists. In the HiTOP system, psychopathology is grouped hierarchically from super-spectra, spectra, and subfactors at the upper levels to homogeneous symptom components and maladaptive traits and their constituent symptoms, and maladaptive behaviors at the lower levels. HiTOP has the potential to improve clinical outcomes by planning treatment based on symptom severity rather than heterogeneous diagnoses, targeting treatment across different levels of the hierarchy, and assessing distress and impairment separately from the observed symptom profile. Assessments can be performed according to this framework with the recently developed HiTOP-Self-Report (HiTOP-SR). Examples of how to use HiTOP in clinical practice are provided for the internalizing spectrum, including the use of the Unified Protocol and other modularized treatments, measurement-based care, psychopharmacology, and in traditionally underserved populations. Future directions are discussed including HiTOP’s use in further developing transdiagnostic treatments, extending the model to include other information such as environmental factors, establishing the treatment utility of clinical assessment for the HiTOP-SR, developing new treatments, and disseminating the model.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".