A framework for representing and solving NP search problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the existence of effective interventions for anxiety disorders, relapse--or the return of fear--presents a significant problem for patients and clinicians in the longer term. The present paper draws on the experimental and clinical behavioural literature, reviewing the mechanisms by which the return of fear can occur. The aim of the paper was to generate a list of treatment recommendations for clinicians aimed at reducing relapse in successfully treated anxiety disorders. Clinical and experimental literature on the mechanisms of renewal, reinstatement, spontaneous recovery and reacquisition are reviewed. These are linked with the clinical and experimental literature on the return of fear in successfully treated anxiety. A list of recommendations to assist in reducing the probability of relapse in successfully treated anxiety is presented. This list includes methods for use in behavioural (exposure) treatment of anxiety disorders that aim to enhance clinical outcomes. Despite the significant problem of relapse in successfully treated anxiety, there are methods available to reduce the probability of relapse through return of fear. Clinicians engaging in treatment of anxiety disorders should be mindful of these methods to ensure optimal patient outcome.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it