Clinical Interpretation of Outcome Measures Generated From a Lumbar Computerized Adaptive Test
Bibliographic record
Abstract
BACKGROUND: A computerized adaptive test (CAT) provides a way of efficiently estimating functional status in people with specific impairments. OBJECTIVE: The purpose of this study was to describe meaningful interpretations of functional status (FS) estimated using a lumbar CAT developed using items from the Back Pain Functional Scale (BPFS) and selected physical functioning items. Design and Setting This was a prospective longitudinal cohort study of 17,439 patients with lumbar spine impairments in 377 outpatient rehabilitation clinics in 30 states. Outcome Measures Patient self-reports of functional status were assessed using a lumbar CAT (0-100 scale). METHODS: Outcome data were interpreted using 4 methods. First, the standard error of the estimate was used to construct a 95% confidence interval for each CAT estimated score. Second, percentile ranks of FS scores were presented. Third, 2 threshold approaches were used to define individual patient-level change: minimal detectable change (MDC) and clinically important change. Fourth, a functional staging model, the Back Pain Function Classification System (BPFCS), was developed and applied. RESULTS: On average, precision of a single score was estimated by FS score+/-4. Based on score distribution, 25th, 50th and 75th percentile ranks corresponded to intake FS scores of 44, 51, and 59, and discharge FS scores of 54, 62, and 74, respectively. An MDC(95) value of 8 or more represented statistically reliable change. Receiver operating characteristic analyses supported that changes in FS scores of 5 or more represented minimal clinically important improvement. The BPFCS appeared clinically logical and provided insight for clinical interpretation of patient progress. LIMITATIONS: The BPFCS should be assessed for validity using prospective designs. CONCLUSIONS: Results may improve clinical interpretation of CAT-generated outcome measures and assist clinicians using patient-reported outcomes during physical therapist practice.
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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 | 0.000 |
| 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) | 0.000 | 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".