Scale for retrospective assessment of immediate concussion symptoms
Bibliographic record
Abstract
The retrospective diagnosis of concussion is often missed by clinicians. We present a brief scale for retrospective assessment of the immediate concussion symptoms (ICS) to facilitate the diagnosis of patients without visible head injury or full loss of consciousness. We administered the scale to 90 survivors of car accidents (mean age 42.0, SD=13.6; 33 males, 57 females) at 2 to 33 months after their accident. Our scale consists of 6 items and these were endorsed by the following % of our respondents: feeling dazed (64.4% of our 90 respondents), stunned (73.3%), confused (70.0%), disoriented (62.2%), dizzy (57.8%), and loss of consciousness (22.2%). The statistical properties of the scale are satisfactory (Cronbach alpha = 0.74). The scale correlates with post-accident insomnia (r=0.28), depression (r=0.29), and also with Rivermead measure of the chronic post-concussion syndrome (r=0.34). The ICS scale could be used as a starting point in longitudinal research with brain imaging procedures to evaluate the stages of recovery from the initial concussion. Attached are the English, Spanish, French, German, Italian, Russian, and Czech versions of our scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".