Pelvic Pseudolesions After Inguinal Hernioplasty Using Prosthetic Mesh
Bibliographic record
Abstract
PURPOSE OF STUDY: To investigate frequency and morphology of focal pelvic lesions (FPLs) in patients after open inguinal hernioplasty with a prosthetic mesh. MATERIALS, METHODS AND PROCEDURES: Patients who had open prosthetic inguinal hernioplasties between 1999 and 2004 and subsequent pelvic computed tomography were identified. Computed tomography of each patient was evaluated by 2 observers. The presence of an FPL at the internal inguinal ring (IIR) and its shape, size, and attenuation were recorded. The findings were compared with the type of surgical mesh used for the repair. RESULTS: There were 93 patients, 86 men, with a mean age of 62.4 years (range, 14-89 years) who underwent 96 hernioplasties, with plug or flat mesh used in 71 and 25 cases, respectively. There were 96 computed tomographies obtained between 1 and 46 months (mean, 15.4 months) after surgery. Focal pelvic lesions were identified in 69 (72%) of 96 cases. Focal pelvic lesions were found in 63 (89%) of 71 cases repaired with a plug, but in only 6 (24%) of 25 cases repaired with a flat mesh (P < 0.0001). One hundred percent of FPLs corresponded to the surgical site and were located deep to the IIR. Focal pelvic lesions were ovoid or round in 65 (94%) and 4 (6%) cases, respectively; all were well defined. Focal pelvic lesions had a mean diameter of 2.4 cm (range, 1.3-3.9 cm) and mean attenuation value of 17 Hounsfield units (range, -4 to 64 Hounsfield units). CONCLUSIONS: A low attenuation, ovoid, or round FPL located at the IIR is a common postoperative finding in patients after open inguinal hernioplasty performed with a plug mesh.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".