Quantifying Post-Accident Neurological Symptoms Other than Concussion
Bibliographic record
Abstract
Introduction:The Rivermead scale is used frequently to assess the post-concussion syndrome in patients after car accidents, but these patients also experience other post-accident neurological symptoms, other than concussion.This article presents a new scale to measure these symptoms and examines its clinical correlates.Materials and Methods: 89 patients applying for compensation with their car insurer after an MVA were interviewed (mean age 42.0, SD=14.0,34 males, 55 females) via the Rivermead Post-Concussion Symptoms Questionnaire, Brief Pain Inventory, Insomnia Severity Index, and the scale for retrospective assessment of the Immediate Concussion Symptoms (ICS).They were also administered the first version of the scale listing chronic post-MVA neurological symptoms (PMNS) which consists of items dealing with hand tremor, impaired muscular control over limbs, tingling, numbness, or reduced feeling in the limbs, and incontinence. Results:The most frequently reported symptoms on the PMNS scale were numbness in the limbs (67.4%), tingling in the limbs (67.4% of the patients), impaired muscular control over leg (44.9%), hand tremor (42.7%), and impaired muscular control over arm or hand (40.4%).The PMNS scale (mean 10.4,SD=7.1, Cronbach's alpha=.82)significantly correlated with Rivermead score (r=.47), with measures of accident related pain (r=.39), insomnia (r=.36), number of previous car accidents (r=.28), the syndrome of word finding difficulty (r=.22), with the accident related PTSD (r=.33), and with depressive mood (r=.40), anger (r=.29), and anxiety (r=.27), but not with age and gender (p>.05).The Cronbach coefficient of internal consistency of the PMNS was satisfactory (.82). Conclusions:The post-accident neurological symptoms (as represented by PMNS scale scores) correlate with the Rivermead, pain, impaired sleep and mood.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".