Gutierrez Questionnaire for Assessments of Patients after Car Accidents
Bibliographic record
Abstract
Background: The polytrauma pattern exhibited by survivors of car accidents typically includes pain, pain related insomnia, PTSD, post-concussion and whiplash syndrome, depression, generalized anxiety, and driving anxiety.The Gutierrez questionnaire was developed to facilitate assessment of these symptoms in clinical settings and in litigations involving car insurance companies.This study evaluates its psychometric properties, the frequencies of polytrauma symptoms, and correlations among these polytrauma symptoms.Method: The Gutierrez questionnaire was administered to 100 patients (48 men, 52 women).Their age ranged from 18 to 85 years with the average at 42.4 years, SD=15.9.Their motor vehicle accident (MVA) occurred, on the average, 11.3 months ago (SD=7.7;range 1 to 34 months).Results: All patients reported some post-MVA pain, 97% reported difficulties falling or staying asleep, all reported some degree of post-concussion syndrome and of whiplash syndrome, 97% also reported generalized anxiety, 88% depression, and all reported some degree of post-MVA driving anxiety.PTSD was suggested by symptoms such as nightmares (52 % of patients), generalized anxiety, and avoidance of driving (84% of patients avoided driving). Discussion and Conclusions:The results indicate a polytraumatic symptom pattern of post-MVA patients that consists of pain, insomnia, post-concussion syndrome, whiplash syndrome, PTSD, depression, generalized anxiety, and driving anxiety.This pattern can be easily assessed by Gutierrez questionnaire.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".