Sensitivity and Specificity of Ultrasonography in Early Diagnosis of Metatarsal Bone Stress Fractures: A Pilot Study of 37 Patients
Bibliographic record
Abstract
OBJECTIVE: To date, early diagnosis of stress fractures depends on magnetic resonance imaging (MRI) or bone scan scintigraphy, as radiographs are usually normal at onset of symptoms. These examinations are expensive or invasive, time-consuming, and poorly accessible. A recent report has shown the ability of ultrasonography (US) to detect early stress fractures. Our objective was to evaluate sensitivity and specificity of US versus dedicated MRI (0.2 Tesla), taken as the gold standard, in early diagnosis of metatarsal bone stress fractures. METHODS: A case-control study from November 2006 to December 2007 was performed. All consecutive patients with mechanical pain and swelling of the metatarsal region for less than 3 months and with normal radiographs were included. US and dedicated MRI examinations of the metatarsal bones were performed the same day by experienced rheumatologists with expertise in US and MRI. Reading was undertaken blind to the clinical assessment and MRI/US results. RESULTS: Forty-one feet were analyzed on US and dedicated MRI from 37 patients (28 women, 9 men, mean age 52.7 +/- 14.1 yrs). MRI detected 13 fractures in 12 patients. Sensitivity of US was 83%, specificity 76%, positive predictive value 59%, and negative predictive value 92%. Positive likehood ratio was 3.45, negative likehood ratio 0.22. CONCLUSION: In cases of normal radiographs, US is indicated in the diagnosis of metatarsal bone stress fractures, as it is a low cost, noninvasive, rapid, and easy technique with good sensitivity and specificity. From these data, we propose a new imaging algorithm including US.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".