Management of ankle disorders during pregnancy: evaluation of 96 cases
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to suggest a safe management method for the diagnosis and treatment of ankle sprains in pregnant patients. METHODS: Between November 2005 and January 2013, 96 pregnant patients with ankle sprains referred to the department of orthopedics and traumatology were evaluated, retrospectively. The Ottawa ankle rules were used to assess the need for radiologic evaluation. Radiological procedures: Surface USG, X-ray (0,6 mGy, mortise view), MRI (T1 and STIR) and fluoroscopy with 0,8 mGy/s doses 0,4 ms single shot views in surgery room. The results of the operated patients were evaluated with AOFAS scoring system. RESULTS: Forty-four (45,8%) patients were treated with conservative methods and there was no need for radiological evaluation. USG was used in 17 (17,7%), MRI in 24 (25%), X-ray in 4 (4,1%) and both USG and MRI in 7 (7,2%) patients during diagnosis. An algorithm was created for the diagnosis and treatment of pregnant patients with ankle sprains. No complications due to radiological and surgical procedures occurred over pregnancies. The AOFAS score was 83 (65-100) in the operated patients. CONCLUSION: There is no standard management method for the diagnosis and treatment of pregnant patients with ankle sprains. The algorithm presented in this study may be useful. Good results can be obtained with an appropriate preparation and surgical technique.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".