Additional file 3 of The impact on functioning of second-generation antipsychotic medication side effects for patients with schizophrenia: a worldwide, cross-sectional, web-based survey
Bibliographic record
Abstract
Additional file 3. Table a. Sample characteristics by region. Table b. Mean Side Effect Scores as measured by the Glasgow Antipsychotic Side-Effect Scale (GASS), All Countries Combined by Age and Overall. Table c. Mean Side Effect Scores as measured by the Glasgow Antipsychotic Side-Effect Scale (GASS), All Countries Combined by Gender and Overall. Table d. Mean Severity (VAS Scales) of Key Side Effects’ Impact on Functioning (Subset analysis), All Countries Combined by Employment Status and Overall. Table e. Linear Regression Model of Activating Side Effects, Demographics, and Time Since Diagnosis on HRQoL (Model A). Table f. Linear Regression Model of Sedating Side Effects, Demographics, and Time Since Diagnosis on HRQoL (Model B). Table g. Linear Regression Linear Regression Model of Other Side Effects, Demographics, and Time Since Diagnosis on HRQoL (Model C). Table h. Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF) Item Descriptive Statistics, All Countries Combined by Gender and Overall. Table i. Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF) Item Descriptive Statistics, All Countries Combined by Employment Status and Overall
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.594 | 0.053 |
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".