Diagnostic Stability of Psychiatric Disorders in Re-Admitted Psychiatric Patients in Kerman, Iran
Bibliographic record
Abstract
BACKGROUND: Several studies have evaluated the stability of psychiatric diagnosis follow in readmission of patients in psychiatric hospitals. However, there is little data concerning this matter from Iran. This study is designed to evaluate this diagnostic stability of the commonest psychiatric disorders in Iran. OBJECTIVES: The objective of this study was to determine the long-term diagnostic stability of the most prevalent psychiatric disorders among re-admitted patients at the Shahid Beheshti teaching hospital in Kerman, Iran. PATIENTS &METHODS: This study was based on 485 adult patients re-admitted at the Shahid Beheshti hospital between July and November 2012.All of the diagnoses were made according to DSM IV TR.Prospective and retrospective consistency and the ratio of patients who were obtained a diagnosis in at least 75%, 100% of the admissions were calculated. RESULTS: The most frequent diagnoses at the first admission were bipolar disorder (48.5%) and Major depressive disorder (18.8%). The most stable diagnosis was bipolar disorder (71% prospective consistency, 69.4% retrospective consistency). Schizoaffective disorder had the greatest diagnostic instability (28.5% prospective consistency, 16.6% retrospective consistency). CONCLUSIONS: Among the cases evaluated, bipolar disorder had the most stability in diagnosis and the stability of schizoaffective disorder was poor.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".