Additional file 1 of Long-term recovery of sensorimotor functions and prediction of participation in survivors of critical illness: a prospective cohort study
Bibliographic record
Abstract
Supplementary material 1: Figure 1 Distribution of the Reintegration to Normal Living Index (RNLI) total score. Table 1 Correlation coefficients for the sensorimotor outcome measures at follow-up and the Reintegration to Normal Living Index at follow-up. Figure 2 Receiver Operating Characteristic (ROC) curve illustrating the predictive performance of the MiniBESTest for distinguishing between good and poor long-term participation. The area under the curve (AUC) is 0.6727 (95% CI 0.595–0.751), indicating fair discriminatory ability. The optimal threshold of 9.5, determined using the Youden Index, yields a sensitivity of 0.546 and a specificity of 0.792. Figure 3 Model Performance of the selected model with physical outcomes Linear model with Box-and-Block-Test, Five-Times-Sit-to-Stand-Test, Mini Balance Evaluation Systems Test (MiniBEST) and the muscle strength measured by the Medical Research Council (MRC) sum score at Visit 1 (V1) describing the Reintegration of Normal Living Index in % (RNLI_p). Figure 4 Model Performance of the extended selected model linear Model with depression, duration of mechanical ventilation, sex, cerebral ischemia, Elixhauser comorbidity index, Mini Balance Evaluation Systems Test (MiniBEST), and Montreal Cognitive Assessment (MoCA) describing the Reintegration of Normal Living Index in % (RNLI_p). Table 2 Parameterwise shrinkage factors and shrinkage-adjusted estimates.
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 | 0.025 |
| 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) | 0.651 | 0.049 |
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".