Validity of the Patient Specific Functional Scale in Patients following Upper Extremity Nerve Injury
Bibliographic record
Abstract
PURPOSE: This study evaluated the validity of the Patient Specific Functional Scale (PSFS) in patients with upper extremity nerve injury. METHODS: Following Research Ethics Boards (REB) approval, we included English-speaking adults, with greater than 6 months after an upper extremity nerve injury. Patient reported questionnaires included: PSFS, 36-item short-form health survey (SF-36), Disabilities of the Arm, Shoulder and Hand (DASH), McGill Pain Questionnaire, Pain Catastrophizing Scale (PCS) and Pain Disability Index (PDI). Statistical analyses evaluated the relationships among the outcome measures and the independent variables (age, gender, nerve injured, time since injury, work status, worker's compensation/litigation). Linear regression was used to evaluate the variables that predicted the PSFS. RESULTS: There were 157 patients (53 women, 104 men); median time since injury of 14 months. The mean ± SD scores were: PSFS 3.1 ± 2.3, DASH 44 ± 22, PCS 16 ± 15, pain intensity 4.2 ± 3.0, pain rating index 13 ± 11, PDI 28.3 ± 17.6 and SF-36 component scores physical (41.8 ± 8.7) mental (45.9 ± 12.6). There were moderate correlations between the PSFS and the DASH, and the SF-36 physical role domain. The PSFS was significantly lower in brachial plexus injuries. The final model explained 20.7 % of the variance and independent variables were DASH, nerve injured and age. CONCLUSION: This study provides evidence of construct validity of the PSFS for patients with upper extremity nerve injury. The PSFS is a valid method to assess functional limitations identified by the individual and can be completed in a shorter period of time than the DASH.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".