Prognostic ability of the sTarT back screening tool for disability and pain intensity outcomes in older adults with low back pain seeking chiropractic care: a multi-national external validation study
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is common among older adults, and it is a frequent reason for seeking chiropractic care. The STarT Back Screening Tool (SBT) was developed to stratify patients with LBP into low, medium, and high-risk treatment pathways, so that the treatment can be matched to each participant's risk profile. But its prognostic performance varies across settings and populations. No studies have focused on the SBT's utility as a stratified-care tool in older adults with LBP in a chiropractic setting. Therefore, our aim was to evaluate the ability of the SBT to predict three-, six-, and 12-month disability and pain outcomes in older adults (≥55 years) with a new episode of LBP consulting chiropractors in the Netherlands, Sweden, and Australia. METHODS: This was a secondary analysis of the Back Complaints in Older Adults - Chiropractic (BACE-C) cohort. Participants visiting chiropractors with LBP completed baseline questionnaires for demographic and clinical characteristics, including the SBT. Follow-up questionnaires assessed disability (Roland Morris Disability Questionnaire (RMDQ)) and pain intensity (11-point Numerical Rating Scale (NRS)). "No improvement" on disability and pain intensity was defined as less than 30% reduction in baseline scores. We used logistic regression models to estimate discrimination metrics including the area under the receiver operating characteristic curve (AUC). Subgroup analyses were conducted by country, sex, and LBP duration; sensitivity analyses employed alternative "no improvement" definitions and linear regression on continuous outcome scores. RESULTS: A total of 738 participants were included. The mean age of the study sample was 66.2 ± 7.5 years and 50.9% of the participants were female. The SBT showed poor discrimination for predicting no improvement in disability and pain intensity. All AUC values were below 0.60 regardless of whether SBT risk subgroups (i.e. low/medium/high) or the SBT sum score were used. Subgroup and sensitivity analyses did not meaningfully improve discrimination. CONCLUSION: The SBT presented limited prognostic ability to predict outcomes of disability and pain intensity in older adults with LBP in a chiropractic setting. These findings suggest insufficient evidence for the prognostic ability of the SBT risk stratification tool. Future research should explore reasons behind the limited prognostic accuracy and consider potential modifications or alternative tools.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".