Athletic groin pain: a systematic review of surgical diagnoses, investigations and treatment
Bibliographic record
Abstract
INTRODUCTION: Athletic groin pain requiring surgery remains a diagnostic and therapeutic challenge. This systematic review aims to identify the most common causes of groin pain in athletes requiring surgery. Additionally, it aims to further characterise their susceptible athlete profiles, common physical examination and imaging techniques, and surgical procedures performed. This will enable the orthopaedic sports medicine clinician/surgeon to best treat these patients. MATERIALS AND METHODS: The electronic databases MEDLINE, PubMed and EMBASE were searched from database inception to 13 August 2014 for studies in the English language that addressed athletic groin pain necessitating surgery. The search was updated on 4 August 2015 to find any articles published after the original search. The studies were systematically screened and data were abstracted in duplicate, with descriptive data presented. RESULTS: A total of 73 articles were included within our study, with data from 4655 patients abstracted. Overall, intra-articular and extra-articular causes of groin pain in athletes requiring surgery were equal. The top five causes for pain were: femoroacetabular impingement (FAI) (32%), athletic pubalgia (24%), adductor-related pathology (12%), inguinal pathology (10%) and labral pathology (5%), with 35% of this labral pathology specifically attributed to FAI. CONCLUSIONS: Given the complex anatomy, equal intra-articular and extra-articular contribution, and potential for overlap of clinical entities causing groin pain leading to surgery in athletes, further studies are required to ascertain the finer details regarding specific exam manoeuvres, imaging views and surgical outcomes to best treat this patient population.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".