Using Intake and Change in Multiple Psychosocial Measures to Predict Functional Status Outcomes in People With Lumbar Spine Syndromes: A Preliminary Analysis
Bibliographic record
Abstract
BACKGROUND: Managing patients with lumbar spine syndromes who are seeking outpatient physical therapy represents a complex problem where psychosocial constructs such as fear-avoidance beliefs regarding physical activities or work activities, somatization, and depressive symptoms may affect functional status (FS) outcomes. OBJECTIVE: The purpose of this study was to determine whether intake or changes in fear-avoidance beliefs regarding physical or work activities, somatization, and depressive symptoms assessed simultaneously affect FS outcomes prediction. DESIGN: This study was a secondary analysis of prospectively collected, longitudinal, observational cohort data. METHODS: Data analyzed were from adult patients (n=323) with lumbar syndromes classified as elevated versus not elevated on single-item screening instruments for fear-avoidance beliefs regarding physical or work activities, somatization, and depressive symptoms at intake and discharge. Prediction of minimal clinically important difference in FS was assessed separately for intake and change from intake to discharge classifications using logistic regression models controlling for important variables. RESULTS: Intake and change models were strong (McFadden rho-squared values=.31 and .49, respectively). Patients classified as not elevated in fear-avoidance beliefs regarding physical activities but elevated in fear-avoidance beliefs regarding work activities, somatization, and depressive symptoms at intake were 5 out of 100 times less likely to report clinically important outcomes compared with being elevated in each measure. Patients not elevated in fear-avoidance beliefs regarding work activities and somatization at intake and discharge were 8 to 14 times more likely to report clinically important outcomes compared with being elevated in each measure. LIMITATIONS: Sample size was limited. Data analyses were retrospective with no control of missing data. CONCLUSIONS: Combinations of multiple psychosocial constructs were important predictors of FS outcomes and may assist patient management by: (1) identifying patients with elevated psychosocial constructs at intake and (2) tracking change in psychosocial variables for improved outcomes prediction. This model may prove helpful for future clinical and research applications to determine optimal psychosocial screening methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".