Slipped capital femoral epiphysis (SCFE) detected in a chiropractic office: a case report.
Bibliographic record
Abstract
OBJECTIVE: To report on a case of slipped capital femoral epiphysis (SCFE), which is a somewhat rare condition but one that can present in a chiropractic clinic, particularly one with a musculoskeletal scope of practice. CASE: This is a single case report of a 16-year-old adolescent male patient who presented with an 18-month history of hip pain. Radiographs originally ordered by the patient's family physician were read by the medical radiologist as "unremarkable." The family physician diagnosed the patient with tendonitis. TREATMENT: After reviewing the radiographs and examining the patient, the chiropractor suspected a SCFE that was confirmed with a repeat radiographic examination. The patient was referred back to his family physician with a diagnosis of SCFE and recommendation for orthopedic surgical consultation. The patient was subsequently treated successfully with surgical reduction by in situ pinning. CONCLUSION: The prognosis for the SCFE patient when diagnosed early and managed appropriately is good. The consequences of a delay in the diagnosis of SCFE are an increased risk of further slippage and deformity, increased complications such as avascular necrosis and chondrolysis and increased likelihood of degenerative osteoarthritis of the involved hip later in life. The diagnosis and appropriate management of SCFE is where the chiropractor has an important role to play in the management of this condition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 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".