Establishing a trait anxiety threshold that signals likelihood of anxiety disorders
Bibliographic record
Abstract
Evidence suggests that the state trait inventory for cognitive and somatic anxiety (STICSA) may be a more pure measure of anxiety than other commonly used scales. Further, the STICSA has excellent psychometric properties in both clinical and nonclinical samples. The present study aimed to extend the utility of the STICSA-Trait version by identifying a cut-off score that could differentiate a group of clinically diagnosed anxiety disorder patients (n=398) from a group of student controls (n=439). Two receiver operating characteristic curve analyses indicated cut-off scores of 43 (sensitivity=.73, specificity=.74, classification accuracy=.74) and 40 (sensitivity=.80, specificity=.67, classification accuracy=.73), respectively. In a large community sample (n =6685), a score of 43 identified 11.5% of individuals as probable cases of clinical anxiety, while a score of 40 identified 17.0% of individuals as probable cases of clinical anxiety. As a result of differences in sensitivity and specificity, the present findings suggest a cut-off score of 43 is optimal to identify probable cases of clinical anxiety, while a cut-off score of 40 is optimal to screen for the possible presence of anxiety disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".