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Record W2537898761 · doi:10.1155/2016/4713137

Bilateral Simultaneous Quadriceps Tendon Rupture in a 24-Year-Old Obese Patient: A Case Report and Review of the Literature

2016· article· en· W2537898761 on OpenAlexaff
Abdulaziz Aljurayyan, Bayan Ghalimah, Lawrence J. Lincoln

Bibliographic record

VenueCase Reports in Orthopedics · 2016
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsMcGill UniversitySt Mary's Hospital Centre
Fundersnot available
KeywordsMedicineQuadriceps tendonEmergency departmentPhysical examinationSurgeryMagnetic resonance imagingRehabilitationPhysical therapyTendonRadiology

Abstract

fetched live from OpenAlex

Introduction . Simultaneous bilateral quadriceps tendon ruptures (SBQTR) are uncommon knee injuries and most frequently occur in male patients, over 50 years of age. It can be associated with one or more predisposing risk factors like obesity, steroids use, and hyperparathyroidism. The main focus of this paper is to review SBQTR in obese patients. Case Report . We are reporting the youngest patient in the literature to date, a 24-year-old obese male patient, who presented to the emergency department complaining of bilateral knee pain and inability to walk after a fall during a basketball game. His clinical examination revealed the presence of a palpable suprapatellar gap and loss of knee extension bilaterally. Magnetic resonance imaging (MRI) confirmed that both of his quadriceps tendons were ruptured. A day after his diagnosis, the patient underwent successful operative repair followed by rehabilitation. At the two-year follow-up, the patient had full strength of both quadriceps muscles with no extension lag. Conclusion . The diagnosis of SBQTR can be challenging. Early diagnosis and treatment are associated with better functional outcome compared to delayed treatment. Physicians should have a high index of clinical suspicion in order not to miss such an injury and achieve favourable outcomes.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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 designCase report
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

Citations11
Published2016
Admission routes1
Has abstractyes

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