1404 – Impacts Of Mental Retardation On Inpatient Psychiatric Care Delivery Of Bipolar Disorder - a National-wide Health Insurance Claims Data Analysis
Bibliographic record
Abstract
Background Mental retardation can complicate the clinical course and outcome of bipolar disorder. How mental retardation affects the inpatient care of bipolar disorder warrants further investigation. Method Information regarding demographic characteristic, pre-admission use of outpatient services, medical co-morbidities and indices of inpatient health resources use (length of admission, hospitalization expenses and use of psychotrophic medications) of all individuals diagnosed with bipolar disorder and mental retardation first admitted between 2000 and 2010 was extracted from a total population claims database in Taiwan and compared with those admitted during the same period due to bipolar disorder. Confounding factors affecting health utilization, including age, differences in hospital payment standard and medical cormobidity, were controlled by multivariate analysis. Results 451 and 13,513 bipolar patients with and without mental retardation were identified during the study period. For the index admission, bipolar individuals with mental retardation were younger, had longer hospital stay with higher total expenditures, and tended to be transferred for continual inpatient treatment. They also received smaller dosage of antipsychotics, lithium and benzodiazepines. Although the number of medical co-morbidity did not differ, the prevalence of hypertension and metabolic disturbances was lower among bipolar individuals with mental retardations. Conclusion The diagnosis of mental retardation was indeed associated with longer inpatient hospitalization and increased total cost of hospitalization expenses, despite being younger, with less metabolic imbalance and receiving less psychotrophic medications. Implications for the long-term course of bipolar disorder need to be confirmed by longitudinal follow-up studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".