High-resolution ultrasound of spigelian and groin hernias: a closer look at fascial architecture and aponeurotic passageways
Bibliographic record
Abstract
From the clinical point of view, a proper diagnosis of spigelian, inguinal and femoral hernias may be relevant for orienting the patient's management, as these conditions carry a different risk of complications and require specific approaches and treatments. Imaging may play a significant role in the diagnostic work-up of patients with suspected abdominal hernias, as the identification and categorization of these conditions is often unfeasible on clinical ground. Ultrasound imaging is particularly suited for this purpose, owing to its dynamic capabilities, high accuracy, low cost and wide availability. The main limitation of this technique consists of its intrinsic operator dependency, which tends to be higher in difficult-to-scan areas such as the groin because of its intrinsic anatomic complexity. An in-depth knowledge of the anatomy of the lower abdominal wall is, therefore, an essential prerequisite to perform a targeted ultrasound examination and discriminate among different types of regional hernias. The aim of this review is to provide a detailed analysis of the fascial architecture and aponeurotic passageways of the abdominal wall through which spigelian, inguinal and femoral hernias extrude, by means of schematic drawings, ultrasound images and video clips. A reasoned landmark-based ultrasound scanning technique is described to allow a prompt and reliable identification of these pathologic conditions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".