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Record W4415718655 · doi:10.14740/jh2084

Bilateral Avascular Necrosis of the Hips in a Patient With Sickle Cell Trait and Chronic Alcohol Use

2025· article· en· W4415718655 on OpenAlexvenueno aff
Sarah Al-Zaher, Janan Niknam, Sivarama K Kotikalapudi

Bibliographic record

VenueJournal of Hematology · 2025
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAvascular necrosisSickle cell traitOrthopedic surgeryFemoral headSickle cell anemiaNonsteroidalDiseaseRange of motion

Abstract

fetched live from OpenAlex

Sickle cell trait (SCT) is generally considered a benign carrier state, unlike sickle cell disease (SCD), which is frequently associated with complications such as avascular necrosis (AVN). While AVN affects approximately 30% of patients with SCD, it is rarely reported in individuals with SCT and is not well understood. A 29-year-old African American male with SCT presented with progressively worsening hip pain. He reported chronic heavy alcohol use but denied steroid use, trauma or previous sickle cell crises. Physical exam demonstrated severely limited hip range of motion and antalgic gait. Laboratory studies were unremarkable aside from elevated C-reactive protein. Radiographs revealed bilateral femoral head AVN greater than stage II, with partial collapse on the left. He was managed conservatively with nonsteroidal anti-inflammatory drugs (NSAIDs), physical therapy, and counseling on alcohol reduction, and was referred to orthopedics for long-term management. Although rare, AVN can occur in individuals with SCT, particularly when compounded by modifiable risk factors such as chronic alcohol use, which can impair bone remodeling, promote fat embolism, and exacerbate microvascular compromise from intermittent sickling. It highlights the importance of early recognition and intervention in patients with unexplained joint pain, and that clinicians should maintain a high index of suspicion for AVN in SCT patients, especially in the presence of other comorbidities and risk factors.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.237
Teacher spread0.226 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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