Bibliographic record
Abstract
INTRODUCTION: A femoral hernia is a rare, acquired condition, which has been reported in less than 5% of all abdominal wall hernias, with a female to male ratio of 4:1. PRESENTATION OF CASE: We report a case in a female patient who had a previous open inguinal herniorrhaphy three years previously. She presented with right sided groin pain of one month duration. Ultrasound gave a differential diagnosis of a recurrent inguinal hernia or a femoral hernia. A transabdominal preperitoneal repair was performed and the patient made an uneventful recovery. DISCUSSION: Laparoscopic repair of a femoral hernia is still in its infancy and even though the outcomes are superior to an open repair, open surgery remains the standard of care. The decision to perform a laparoscopic trans abdominal preperitoneal (TAPP) repair was facilitated by the patient having previous open hernia surgery. The learning curve for laparoscopic femoral hernia repair is steep and requires great commitment from the surgeon. Once the learning curve has been breached this is a feasible method of surgical repair. This is demonstrated by the fact that this case report is from a rural hospital in Canada. CONCLUSION: Laparoscopic femoral hernia repair involves more time and specialized laparoscopic skills. The advantages are a lower recurrence rate and lower incidence of inguinodynia.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".