Validating the use of three-dimensional ultrasound to detect partial flexor tendon lacerations
Bibliographic record
Abstract
Partial flexor tendon tears are common, but their diagnosis presents a few challenges. The degree of a partial flexor tendon tear necessitating surgical intervention remains under debate. This is primarily due to the lack of a sensitive and accessible imaging modality to assess the depth of a tendon laceration. We suggest the use of three-dimensional ultrasound (3DUS) to provide a more accurate grading of partial flexor tendon tears by providing surgeons with a decision-making tool to identify when surgical intervention is necessary. As a proof of concept, we dissected 6 digits from 2 fresh-frozen cadaveric specimens. Each digit was imaged using 3DUS and MRI to determine if the tendons and tendon lacerations are identifiable in the 3DUS images. This will be performed in preparation for a larger trial where each digit will be randomly assigned to an intact, low-grade laceration, or high-grade laceration group. 3DUS images were collected from each digit. MR images were also collected from each digit to compare the sensitivity of each imaging modality. Further analysis of the images will require a trained radiologist to grade the lacerations in each image, while being blinded to the actual grade of each laceration and comparing the radiologist’s grading of each image to each laceration’s actual grading to assess the sensitivity of 3DUS and MR in detecting partial tendon lacerations. We anticipate that 3DUS imaging to be comparable to MR imaging in detecting partial tendon tears. Future work includes applying load to the affected fingers to investigate whether that will improve partial tendon laceration detection. This work expands on the applications of 3DUS in musculoskeletal imaging and provides clinicians with an accessible tool to accurately detect and grade partial tendon lacerations.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".