An Investigation of Spectrum of Diseases and Medication Use in Discharged Patients with Mental Disorders in the Inner Mongolia Autonomous Region
Bibliographic record
Abstract
Abstract Objective We aim to investigate the inpatient status of patients with mental disorders who had been discharged from psychiatric hospitals in 2016 in the Inner Mongolia Autonomous Region, and through this, to provide theoretical basis for further improving the security system for patients with mental disorders in this region. Method: A two-phased stratified sampling method was used to collect data from selected hospitals. Results: A total of 1646 cases with valid data were obtained. The most common mental diseases were schizophrenia and bipolar disorder; atypical antipsychotic drugs played a dominant role (95.78%) among various antipsychotics, with risperidone being the most frequently used drug. Conclusion: We need to further improve the management of schizophrenia and bipolar disorder, as well as the hospital-community integration mode. Standardized training of residents and further education for clinicians should be enhanced to improve doctors’ clinical skills and promote drug prescribing in a scientific, rational and standardized manner.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".