Validation of the Subjective Neuropsychological Symptoms Scale (SNPSS) in Injured Motorists
Bibliographic record
Abstract
Background: There is a need for scales for standardized assessments of subjective neuropsychological symptoms reported by diverse patient populations such as injured motorists, patients with multiple sclerosis, very severe anorexia, and neurotoxin exposure.This article introduces the Subjective Neuropsychological Symptoms Scale (SNPSS) and describes its validation on a sample of persons injured in high-speed motor vehicle accidents (MVAs). The SNPSS consists of post-concussion items not included in the Rivermead Post-Concussion SymptomsQuestionnaire (that is, e.g., tinnitus, impaired balance, word finding difficulty), items to assess motor symptoms (e.g., hand tremor), and symptoms frequently observed in patients with spinal injury or deterioration (tingling, numbness, or reduced feeling in the limbs).Method: De-identified file data of 141 post-MVA patients (49 men, 92 women, average age 39.4 years, SD=13.0)included their responses to the SNPSS, the Rivermead Post-Concussion Symptoms Questionnaire, Insomnia Severity Index, the PCL-5 measure of PTSD according to DSM5,the ratings of worst, least, and average pain on the Brief Pain Inventory and ratings of depression, anger, and anxiety on the Whiplash Disability Questionnaire.The patients' responses to the SNPSS and the Rivermead were compared to those of a sample of 23 normal controls (11 men, 12 women, average age 45.0 years, SD=21.2).Results: Average SNPSS score of patients (20.3 points, SD=11.3) was significantly higher than of normal controls (average of 2.5 points, SD=4.8): the magnitude of this relationship (r=.51, p<.001) indicates satisfactory criterion validity of the SNPSS on post-MVA patients.With respect to convergent validity, the SNPSS correlated significantly (r=.79, p<.001) with the Rivermead scores.The SNPSS also correlated significantly with clinical variables often associated with neurological trauma in injured motorists: pain (r=.37), insomnia (r=.45), and PTSD (r=.56).Cronbach alpha coefficient of the SNPSS is very satisfactory (.90).Discussion and Conclusions: Criterion and convergent validity data of SNPSS on injured motorists in this study are satisfactory.Validation data from populations other than injured motorists are much needed, e.g., survey data from patient groups with neurological disease such as multiple sclerosis or those accidentally exposed to neurotoxins.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".