Exploring sex as a moderator of other prognostic variables in whiplash associated disorder: An observational study
Bibliographic record
Abstract
BACKGROUND: Prognostic variables for assessing people with whiplash associated disorder (WAD) following a motor vehicle collision (MVC) have been evaluated in numerous studies. However, there is minimal evidence assessing how these variables may differ between males and females. QUESTION/PURPOSE: 1) To assess if the sex of a person interacts with known prognostic variables within the development of chronic WAD. 2) To determine if commonly used outcome measures used in the assessment of chronic WAD differ between sexes. METHODS: The study was a secondary analysis of an observational study with an inception cohort immediately following an MVC in an emergency department in Chicago, IL, USA. Ninety-seven adults aged 18 to 60 (mean 34.7 years old; 74% female) participated in the study. The primary outcome was long-term disability as determined by Neck Disability Index (NDI) scores at 52-weeks post-MVC. Data was collected at baseline (less than 1-week), 2, 12, 52-weeks post MVC. Hierarchal linear regression was used to determine significance (ΔF-score, p < 0.05) and R2 for each of the variables. The primary variables of interest were sex of the participant, age, baseline scores on the numeric pain rating scale (NPRS) and NDI and created interaction terms for sex x z-baseline NPRS and sex x z-NDI. RESULTS: From analysis 1, both NDI (R2 = 8.7%, p < 0.01) and NPRS (R2 = 5.7%, p = 0.02) collected at baseline predicted significant variance in NDI score at 52-weeks. The interaction term of sex x z-NPRS was also significant (R2 = 3.8%, p = 0.04). In analysis 2 the regression models when disaggregated by sex showed that baseline NDI was the significant predictor of 52-week outcome in males (R2 = 22.4%, p = 0.02) while it was the NPRS as the significant predictor in females (R2 = 10.5%, p < 0.01).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".