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Record W1974015516 · doi:10.5505/tjtes.2014.94914

Management of ankle disorders during pregnancy: evaluation of 96 cases

2014· article· en· W1974015516 on OpenAlexaboutno aff
Çetin Işık, Mesut Tahta, Derya Işik, Yusuf Üstü, Mehmet Uğurlu, Nuray Bozkurt, Murat Bozkurt

Bibliographic record

VenueUlusal travma dergisi · 2014
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleRadiological weaponOrthopedic surgeryFluoroscopyTraumatologySurgeryRadiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.346
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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