Chronic pain due to Little Leaguer's Shoulder in an adolescent baseball pitcher: a case report.
Bibliographic record
Abstract
OBJECTIVE: To describe a case of chronic Little Leaguer's Shoulder in reference to pain presentation, physical capabilities, and recovery time. CLINICAL FEATURES: A 17-year-old, junior baseball pitcher presented with shoulder pain when performing high velocity pitching. Conservative treatment for an assumed soft tissue injury failed to resolve the pain, which was regularly aggravated by pitching, and which subsequently prompted further evaluation, and eventual confirmation of Little Leaguer's Shoulder on subsequent computerized tomography (CT) imaging. INTERVENTION AND OUTCOME: Prior to proper diagnosis, conservative treatment had consisted of activity modification, spinal adjusting, laser therapy, shockwave therapy, Active Release Techniques(®), Kinesiotape,(®) and rehabilitation. Later, rehabilitation, consisting of general muscle and core strengthening, continued for a further six months under the supervision of college athletic trainers. The athlete was able to return to normal pitching duties approximately 12 months later. SUMMARY: In this case, a potentially damaging bone injury masquerading as a simple musculo-tendinous injury created a diagnostic challenge. The patient eventually recovered with rest, time, strengthening, and eventual compliance to prescribed activity modification.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".