Improving Reliability of the Assessment of the Life Course of Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Life course studies of schizophrenia that have used a 3-phase model (onset, course, and outcome) have had their use restricted owing to differences in definition and methodology. The purpose of this investigation was to describe life course data in mathematical terms and to compare the results with the findings from other life course studies. METHOD: The study population was comprised of 128 of 137 people who were first admitted for schizophrenia to 1 of the 2 mental hospitals in Alberta in 1963 and followed until 1997 or death. Patient evaluations were based on retrospective and contemporaneous information collected from the patients and hospital files, treatment records, and family members. Mathematically derived ratings were formulated for course, outcome, and onset (pre-admission years). The distribution of the resulting 8 life course types was compared with profiles drawn from other such studies reported in the literature. RESULTS: The use of mathematical descriptions of onset, course, and outcome produced profiles that did not closely match the results of other investigations, largely owing to inconsistency across studies. Further, the present approach to outcome measurement produced results that were less favourable than those found in other studies. CONCLUSIONS: Studies on the life course of schizophrenia could be made more comparable by specifying mathematically expressed operational definitions of onset, course, and outcome. Nonetheless, the use of the term outcome can be questioned as it implies an assessment at a specific time rather than providing a summary statement of the quality of a life.
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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 | 0.053 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".