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Record W1779922449 · doi:10.1002/jso.24072

Salvage of the proximal femur following pathological fractures involving benign bone tumors

2015· article· en· W1779922449 on OpenAlexaff
Pedro I. Carvallo, Anthony M. Griffin, Peter C. Ferguson, Jay S. Wunder

Bibliographic record

VenueJournal of Surgical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineChondroblastomaInternal fixationCurettageSurgeryFibrous dysplasiaPathologicalBone graftingFemurGiant-cell tumor of bonePathologic fractureRadiologyGiant cellInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: To describe the surgical treatment of patients with a pathologic fracture through a benign tumor of the proximal femur to determine if there is a difference in local recurrence, complications or functional outcome compared to patients with tumors in the same location without pathologic fractures. METHODS: From 1989-2010, of 97 patients, 29 presented with a pathologic fracture (PF) through a proximal femoral benign bone tumor and 68 presented without a pathologic fracture (NPF). Outcomes of the two groups were compared in terms of surgical management, postoperative complications, local recurrence and functional scores. RESULTS: Fibrous dysplasia, giant cell tumor of bone and chondroblastoma were the most common subtypes. Most patients were managed with joint preservation in both PF (86.2%) and NPF (98.5%) groups (P = 0.03). Local recurrence risk was similar for patients in the PF (10.3%) and NPF (8.8%) groups. Mean follow-up was 105.7 months (P = 0.8). Functional outcome scores were high in both groups and not statistically significantly different. CONCLUSIONS: The majority of pathologic fractures through a benign bone tumor of the proximal femur can be successfully treated with curettage, burring, bone grafting and internal fixation without increasing the risk of local recurrence or negatively impacting functional outcome.

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)0.000
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 score0.095
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.337
Teacher spread0.296 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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