A Novel Approach to New-Onset Hemiplegic Shoulder Pain With Decreased Range of Motion Using Targeted Diagnostic Nerve Blocks: The ViVe Algorithm
Bibliographic record
Abstract
Introduction: Hemiplegic shoulder pain (HSP) is the most common pain disorder after stroke with incidence estimates of 30–70% and associated with reductions in function, interference with rehabilitation, and a reduced quality of life. Onset may occur as soon as a week after stroke in 17% of patients. Management of HSP represents a complex treatment pathway with a lack of evidence to support one treatment. The pain has heterogeneous causes. In the acute setting, decreased range of motion in the shoulder can be due to early-onset spasticity, capsular pattern stiffness, glenohumeral pathology, or complex regional pain syndrome (CRPS). As contracture can form in up to 50% of patients after stroke, effective management of the painful shoulder and upper limb with decreased range of motion requires assessment of each possible contributor for effective treatment. The anesthetic diagnostic nerve block (DNB) is known to differentiate spasticity from contracture and other disorders of immobility and can be useful in determining an appropriate treatment pathway. Objective: To create a diagnostic algorithm to differentiate between the causes of HSP in the stiff, painful shoulder in the subacute setting using diagnostic techniques including the Budapest Criteria for CRPS and DNB for spasticity and pain generators. Results: Examination of each joint in the upper extremity with HSP may differentiate each diagnosis with the use of an algorithm. Pain and stiffness isolated to the shoulder may be differentiated as primary shoulder pathology; sensory suprascapular DNB or intra-articular/subacromial injection can assist in differentiating adhesive capsulitis, arthritis, or rotator cuff injury. CRPS may affect the shoulder, elbow, wrist, and hand and can be evaluated with the Budapest Criteria. Spasticity can be differentiated with the use of motor DNB. A combination of these disorders may cause HSP, and the proposed treatment algorithm may offer assistance in selecting a systematic treatment pathway.
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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 | 0.001 |
| 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".