Multidimensional analysis of adult patients’ care trajectories before a first diagnosis of schizophrenia
Why this work is in the frame
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Bibliographic record
Abstract
For patients at high-risk for developing schizophrenia, a delayed diagnosis could be affected, among many reasons, by their patterns of healthcare use. This study aims to describe and generate a typology of patients' care trajectories (CTs) in the 2 years preceding a first diagnosis of schizophrenia, over a medico-administrative database of 3712 adults with a first diagnosis between April 2014 and March 2015 in Quebec, Canada. This study applied a multidimensional approach of State Sequence Analysis, considering together sequences of patients' diagnoses, care settings and care providers. Five types of distinct CTs have emerged from this data-driven analysis: The type 1, shared by 77.6% of patients, predominantly younger men, shows that this group sought little healthcare, among which 17.5% had no healthcare contact for mental disorders. These individuals might benefit from improved promotion and prevention of mental healthcare at the community level. The types 2, 3 and 4, with higher occurrence of mental disorder diagnoses, represent together 19.5% of the study cohort, mostly middle-aged and women. These CTs, although displaying roughly similar profiles of mental disorders, revealed very dissimilar sequences and levels of care providers encounters, primary and specialized care use, and hospitalizations. Surprisingly, patients of these CTs had few consultations with general practitioners. An increased attentiveness for middle-aged patients and women with high healthcare use for mental disorders could help to reduce delayed diagnosis of schizophrenia. This calls for further consideration of healthcare services for severe mental illness beyond those offered to young adults.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 it