Association Between Diagnostic Imaging, Medication Intake, and Health Outcomes in Chronic Whiplash-Associated Disorders: An Observational Study
Bibliographic record
Abstract
Purpose: Studies have demonstrated that medication and diagnostic imaging (DI) use and, more broadly, health care resource utilization, are not necessarily concordant with clinical practice guidelines. However, these studies did not evaluate the concurrent presence of clinical manifestations. This study therefore aimed to investigate the association between medication intake and DI, and health outcomes including pain, disability, physical, and mental health-related quality of life in people with chronic whiplash-associated disorders (WADs). We also aimed to evaluate whether medication intake and DI use differed based on specific presenting clinical manifestations (pain classification category [nociceptive, nociplastic, or neuropathic], psychologic features, pain cognitions, and sleep). Method: This cross-sectional study investigated people attending a multidisciplinary chronic pain centre in Calgary, Canada, between October 2019 and December 2021 who attended for evaluation of chronic symptoms arising from a motor vehicle collision. Participants completed a series of questionnaires that evaluated various health domains (pain intensity/interference; disability; physical and mental health-related quality of life; depression, anxiety, and stress; post-traumatic stress; pain catastrophizing; and sleep) at intake, and anonymized results were entered into a registry database with their informed consent. Results: Different classes of medication intake, use of multiple medications, receiving CT, ultrasound, or MRI scans and multiple DI utilization were associated with worse health outcomes. Increased medication intake was also associated with increased DI utilization. In concert, these results suggest that increased health care resource utilization was associated with worse health outcomes – both physically and psychologically – in chronic WAD. Conclusions: Our results indicate that medication intake and DI were not matched to clinical manifestations, and thus further education of health care providers is warranted to assist with appropriate health care resource utilization. These results also suggest that chronic WAD guidelines are required inclusive of recommendations for appropriate utilization of medication and referral for DI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".