Improving the Rapid and Reliable Diagnosis of Complete Distal Biceps Tendon Rupture
Bibliographic record
Abstract
BACKGROUND: Diagnosis of complete distal biceps tendon rupture (DBTR) is frequently missed or delayed on clinical examination. No single clinical test, including MRI, has demonstrated 100% efficacy in assessing the integrity of the distal biceps tendon. HYPOTHESIS: Combining 3 validated clinical tests for identifying complete rupture can maximize a true-positive diagnosis for complete DBTR without the need for confirmatory soft tissue imaging when performed in concert with other important factors from the history and clinical examination. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: The hook test, the passive forearm pronation (PFP) test, and the biceps crease interval (BCI) test were applied in sequence in conjunction with a standard patient history and physical examination on 48 patients with suspected distal biceps tendon injuries. If results on all 3 special tests were positive for complete rupture, the patient was referred for surgical repair; diagnosis was confirmed intraoperatively. If results on all 3 special tests were negative, diagnosis was confirmed with soft tissue imaging and patients were managed nonoperatively. If results of the 3 tests were not in agreement, soft tissue imaging was used to clarify the disagreement and to confirm the diagnosis. RESULTS: Thirty-five patients had unequivocal results based on history, physical examination, and special tests. Thirty-two tested in agreement positive for complete rupture, which were confirmed intraoperatively. Three tested in agreement negative, with subsequent imaging confirming partial rupture. Thirteen patients had equivocal special test results; soft tissue imaging suggested complete rupture in 10 and partial rupture in 3. CONCLUSION: Application in sequence of the hook test, the PFP test, and the BCI test results in 100% sensitivity and specificity when the outcomes on all 3 special tests are in agreement.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".