Graphical Investigation of Threshold Choice Effect on Odds Ratio Related to Prognostic Factors in Stroke Recovery
Bibliographic record
Abstract
The aim of this study was to assess the effects of the arbitrary choices of threshold-values for dichotomizing not binary factors on the computation of odds ratio (OR) for the identification of prognostic factors, in particular of motor recovery after stroke. Data of a sample of 1000 patients with subacute stroke have been analysed. We considered as dependent variable the effectiveness of neurorehabilitation (i.e. the achieved level of independency in activities of daily living, measured using the Barthel Index, expressed in percentage of the maximum achievable improvement), and as independent variables age, time between stroke acute event and beginning of neurorehabilitation, gender, type of stroke (ischemic vs. haemorragic) and side of hemiparesis. We performed univariate analyses for computing OR with respect to different choices of threshold for dichotomizing age and time from stroke. In this analysis median value of effectiveness was used for dichotomizing subjects in good and poor responders. Then these analyses were repeated also varying the threshold-value of effectiveness. Finally multivariate analyses based on forward binary logistic regression were performed varying at the same time the thresholds of age and time from stroke. With respect to threshold choice, OR-values of age resulted stable, but those of time from stroke resulted more variable. Variability increased when also the threshold chosen for dichotomizing the independent variable was changed. Multivariate analyses showed that these choices could even make not statistically significant the effect of a binary prognostic factor such as gender. In conclusion, OR-values resulted affected by threshold choices. It can increase the difficulties in marking predictions of outcomes after stroke. In this study we reported a possible graphical evaluation of the variability of OR-values with respect of threshold choice, that can be helpful whenever threshold is arbitrary chosen.
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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 | 0.018 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".