Liaison psychiatrist in a specialist diabetes centre
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
Aims and Method To identify psychiatric conditions that may require psychiatric treatment in individuals referred from a diabetes centre to a liaison psychiatry diabetes clinic and to examine the referral pattern and reasons for referral. the study was a prospective case-note survey over a 12-month period. Results The most common reason for referral was low mood (37.5%) and the most common psychiatric diagnosis was adjustment disorder (28.6%). Most individuals were referred by diabetes specialist nurses (53.1%). the majority (85.7%) were seen by a clinical psychologist after an initial assessment by a psychiatrist. Clinical Implications Common mental disorders occur frequently in individuals referred to a liaison diabetes clinic, hence the importance of early diagnosis. the involvement of psychiatrists in specialist diabetes clinics is very limited and better involvement is desirable.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it