PATTERN OF PSYCHIATRIC DISORDERS AT HIGH ALTITUDE: A CROSS-SECTIONAL STUDY FROM SKARDU, PAKISTAN
Bibliographic record
Abstract
OBJECTIVES: To determine the frequency of different psychiatric disorders among patients reporting to psychiatry department and to study their association with age, marital status and education level. STUDY DESIGN: Descriptive cross-sectional study. PLACE AND DURATION OF STUDY: Department of Psychiatry, Combined Military Hospital (CMH) Skardu from September 2021 to February 2022. MATERIALS AND METHODS: A total of one hundred adult individuals, between 21 to 40 years of age, presenting to the Psychiatry department at CMH Skardu, were enrolled in this study after taking written informed consent. All the patients were interviewed by consultant psychiatrist and diagnosis was based on International Classification of Diseases (ICD) version 10. RESULTS: Out of a total of 100 patients, 81 (81%) were married and 19 (19%) were un-married. The mean age of the patients was 26.94 + 4.35 years. The most common psychiatric disorders among patients living at high altitude were depressive episode in 44 patients (44%), followed by adjustment disorders in 30 (30%), anxiety disorder in 12 (12%), dissociative disorder in 9 (9%) and mood disorder in 5 patients (5%). CONCLUSION: Depressive episode and adjustment disorder were the most prevalent psychiatric disorders at high altitude.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".