Naar een gepersonaliseerde kinderen jeugdpsychiatrie met registratiegegevens uit de dagelijkse zorgpraktijk
Bibliographic record
Abstract
BACKGROUND: Studying differences in the course and treatment effects of psychiatric disorders between subgroups of patients can provide suggestions to improve everyday clinical practice. AIM To illustrate how routinely registered data from child and adolescent psychiatry can be used to gain insight into differences in the development of patient groups. METHOD: Multilevel analyses in four subgroups of youths with an autism spectrum disorder (ASD; n = 1681; boys/girls, with/without comorbid psychiatric disorder) to investigate differences in the development of quality of life during the first six months of treatment. RESULTS: Subgroups of youths with ASD showed differences in development of quality of life, which can provide suggestions to establish personalized care. CONCLUSION: Multicenter research in large samples is needed to investigate the robustness of our findings. The 'Research Data Infrastructure', containing routine outcome monitoring and electronic medical record data from more than 117.000 youths in child and adolescent psychiatry, offers a unique opportunity to perform large scale practice based research.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".