Trends in admissions to a child and adolescent neuropsychiatric inpatient unit in the 2007–2017 decade: how contemporary neuropsychiatry is changing in Northwestern Italy
Bibliographic record
Abstract
PURPOSE: Rising levels of psychopathology in the adolescent population have been evidenced in the last few years throughout the Western world. We aim to examine how contemporary neuropsychiatry is changing in Northwestern Italy and how this impacts inpatient services. METHODS: The present research considered the 1177 admissions to a public neuropsychiatric inpatient service in the 2007-2017 decade. The annual percentual change (APC) was analysed for the total admissions, the number of the neurological vs psychiatric admissions, the length of inpatient stay, and the mean age at admission, also accounting for sex differences. The annual trend was also calculated for each diagnosis. RESULTS: The overall number of inpatient admissions decreased significantly (APC = - 5.91), in particular for children under 12 years of age (APC = - 7.23). The rate of neurologic diagnoses significantly decreased (APC = - 26.44), while the length of the inpatient stay (APC = 6.98) and the mean age at admission (APC = 6.69) increased. Among the psychiatric diagnoses, depression significantly rose (APC = 41.89), in particular among female adolescents (APC = 40.30). CONCLUSIONS: These data document a substantial change in the utilization of inpatient neuropsychiatric services for children and adolescents, with a major increase in psychiatric hospitalizations and a parallel decrease in neurological ones. These trends call for greater attention to early preventive intervention in mental healthcare system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".