Diagnostic Accuracy of Echo Envelope Statistical Modeling Compared to B‐Mode and Power Doppler Ultrasound Imaging in Patients With Clinically Diagnosed Lateral Epicondylosis of the Elbow
Bibliographic record
Abstract
Objectives To compare the accuracy of homodyned K quantitative ultrasound (QUS) with that of B‐mode and Doppler ultrasound imaging for discriminating between lateral epicondylosis (LE) and asymptomatic elbows. Methods This prospective study received Institutional Review Board approval, and participants provided written informed consent. Between February 2015 and March 2017, 30 LE elbows in 27 patients and 24 asymptomatic elbows in 13 volunteers underwent B‐mode, Doppler, and radiofrequency ultrasound imaging of the common extensor tendon (CET) and radial collateral ligament (RCL). Two readers classified the elbows independently on the basis of a review of B‐mode and Doppler images. The global and local estimates of QUS parameters (μn, 1/α, andk) were computed in the CET and CET‐RCL regions, respectively, and the area of each region was calculated. A random‐forest classifier identified the most discriminating 3‐parameter combination: CET global estimate of 1/α, CET‐RCL area, and local estimate ofk. Results The patients with LE had a mean age of 50 years (range, 31–66 years), and the volunteers had a mean age of 50 years (range, 37–57 years). The area under the receiver operating characteristic curve, sensitivity, and specificity of reader 1, reader 2, and the QUS‐based model were 0.80 (95% confidence interval [CI], 0.66–0.95), 0.72 (95% CI, 0.56–0.89), and 0.88 (95% CI, 0.72–1.04); 0.79 (95% CI, 0.66–0.93), 0.65 (95% CI, 0.47–0.82), and 0.84 (95% CI, 0.67–1.01); and 0.82 (95% CI, 0.80–0.85), 0.73, and 0.79, respectively. Conclusions An automated, computer‐based QUS technique diagnosed LE with accuracy of 0.82. This technique could provide quantitative biomarkers for the characterization of LE disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".