Hip microinstability diagnosis and management: a systematic review
Bibliographic record
Abstract
PURPOSE: The purpose of this systematic review is to present the most common causes, diagnostic features, treatment options and outcomes of patients with hip micro-instability. METHODS: Three online databases (MEDLINE, Embase, and PubMed) were searched from database inception March 2022, for literature addressing the diagnosis and management of patients with hip micro-instability. Given the lack of consistent reporting of patient outcomes across studies, the results are presented in a descriptive summary fashion. RESULTS: Overall, there were a total of 9 studies including 189 patients (193 hips) included in this review of which 89% were female. All studies were level IV evidence with a mean MINORS score of 12 (range: 10-13). The most commonly used features for diagnosis of micro-instability on history were anterior pain in 146 (78%) patients and a subjective feeling of instability with gait in 143 (81%) patients, while the most common feature on physical examination was the presence of anterior apprehension with combined hip extension and external rotation in 123 (65%) patients. The most common causes of micro-instability were iatrogenic instability secondary to either capsular insufficiency or cam over-resection in 76 (62%) patients and soft tissue laxity in 38 (31%) patients. CONCLUSION: The most common symptom of micro-instability on history was anterior hip pain and on physical exam was pain with hip extension and external rotation. There are many treatment options and when managed appropriately based on the precise cause of micro-instability, patients may demonstrate improved outcomes. LEVEL OF EVIDENCE: IV.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.004 | 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".