Effects of customized foot orthotics on reported disability and analgesic use in patients with chronic low back pain associated with motor vehicle collisions
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare reported disability due to chronic low back pain following a motor vehicle collision between groups of those using customized foot orthotics and those not using orthotics. METHODS: Sixty-six consecutive patients referred from primary care medical physicians for the complaint of chronic (> 3 months) low back pain following a motor vehicle collision were included. Thirty patients received "usual care" that included prescription of an exercise therapy program in addition to analgesics. Thirty-four patients received the same therapy along with customized foot orthotics. All patients completed the Oswestry Disability Index at the initiation of the study and at 8-week follow-up. The number of participants using any type of prescription analgesic for their back pain at baseline and at 8 weeks was also recorded. RESULTS: All patients completed treatment, and the baseline and 8-week questionnaires. Both treatment groups were well matched in terms of age, sex distribution, and duration of low back pain, as well as baseline Oswestry Disability Index score. At 8 weeks, although both groups had improved, the group that used orthotics had a lower Oswestry Disability Index than the usual care group (P < .05), with a smaller proportion of the orthotics group using any form of prescribed analgesics for back pain (P < .05). CONCLUSIONS: In this study, patients with chronic low back pain following a motor vehicle collision who used orthotics in addition to usual care had improved short-term outcomes compared with usual care alone.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".