Prospective Analysis of Relationships of Outcome Measures for Ulnar Neuropathy at the Elbow
Bibliographic record
Abstract
BACKGROUND: We undertook a prospective study to investigate relationships between outcome measures of ulnar neuropathy at the elbow. METHODS: Thirty-one patients (mean age 52.6, range 20-80), with clinically and electrically verified ulnar neuropathy at the elbow, were seen independently by a neurosurgeon and a physiotherapist. All tests were administered to all patients on each visit. Data collected included measures of sensory (monofilament, two-point discrimination, vibration) and motor function (grip, key-pinch, muscle atrophy), pain (visual analogue scale (VAS)) and impact on lifestyle (Levine's questionnaires (function status score--FSS, symptom severity score--SSS)), disability of the arm, shoulder and hand module (DASH) and patient-specific measures (PSM). Parametric and non-parametric correlation and factor analysis were done. RESULTS: Outcome analysis was available for 63 patient visits, with follow-up obtained for 20 patients (mean 8.5 months). Lifestyle and pain instruments (FSS, SSS, DASH, PSM and VAS) all correlated well with each other (r > 0.6, p < .01). DASH was moderately to highly correlated to nine of the 11 measures. Some tests correlated poorly, for example, Semmes-Weinstein monofilament with other sensory measures and muscle atrophy with almost all measures. Factor analysis revealed that there are two principal factors, accounting for 77% of the variance. Factor 1 relates to impact on lifestyle and pain while Factor 2 relates to strength and function. DISCUSSION/CONCLUSIONS: Intraclass measures, particularly ones assessing lifestyle and pain instruments are strongly correlated. Factor analysis revealed two principal factors that account for the majority of the variance; future studies with a larger sample size are needed to validate this analysis.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".