P.032 Development of a new instrument to discriminate orthostatic from non-orthostatic symptoms
Bibliographic record
Abstract
Background: Orthostatic symptoms including dizziness, light-headedness and syncope can be major causes of disability in patients with dysautonomia. Currently there is no validated tool capable of discriminating orthostatic from non-orthostatic constitutional symptoms. Therefore, we developed the Orthostatic Discriminant and Severity Scale (ODSS) to help make this distinction. Objective: Demonstrate validity and reliability of the ODSS. Methods: Convergent and clinical validity were assessed by correlating Orthostatic scores with previously validated tools (Autonomic Symptom Profile (ASP), composite scores of the Orthostatic Hypotension Questionnaire and the total Composite Autonomic Severity Score (tCASS), respectively). Test-retest reliability was calculated using an intra-class correlation coefficient. Results: Orthostatic scores from 23 controls and 5 patients were highly correlated with both the Orthostatic Intolerance index of the ASP (r=0.724;p<0.01) and the composite OHDAS and OHSAS (r=0.552;p<0.01 and r=0.753;p<0.01, respectively), indicating good convergent validity. Orthostatic scores were significantly correlated with tCASS (r=0.568;p<0.01), and the systolic blood pressure change during head-up tilt (r=-0.472;p=0.013). In addition, patients with Neurogenic Orthostatic Hypotension had significantly higher Orthostatic scores than controls (p<0.01) indicating good clinical validity. Test-retest reliability was strong (r=0.954;p<0.01) with an internal consistency of 0.978. Conclusions: Our results, though preliminary, provide empiral evidence that the ODSS is capable of producing a valid and reliable orthostatic score.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".