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Record W2035325067 · doi:10.1002/jso.21542

Clinical and functional outcomes of patients with a pathologic fracture in high‐grade osteosarcoma

2010· article· en· W2035325067 on OpenAlexaffabout
Peter C. Ferguson, Catherine E McLaughlin, Anthony M. Griffin, Robert S. Bell, Benjamin Deheshi, Jay S. Wunder

Bibliographic record

VenueJournal of Surgical Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineOsteosarcomaAmputationRetrospective cohort studyPathologic fractureSurgerySingle CenterPresentation (obstetrics)Trauma centerPathology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: There have been variable reports of outcomes of patients with osteosarcoma and pathologic fractures. The purpose of this study was to document outcomes after management of this clinical entity at a single large oncology center. METHODS: A retrospective review was undertaken of our database between 1989 and 2006. We compared oncologic and functional outcomes of 201 patients with high-grade osteosarcoma without pathologic fractures to 31 patients with pathologic fractures. RESULTS: The rate of amputation in the group with pathologic fracture was significantly higher than the group without fracture (39% vs. 14%, P = 0.001). There was no difference in the rate of local recurrence between groups. The 5-year survival was superior in the group without pathologic fracture (60% vs. 41%, P = 0.0015). For patients with localized disease, 5-year survival was higher in patients without fracture (68% vs. 52%, P = 0.006). Disability as measured by the Toronto Extremity Salvage Score was no different between the groups. Impairment as measured by the Musculoskeletal Tumor Society scores was lower in the group without fracture. CONCLUSIONS: Presentation with a pathologic fracture in osteosarcoma did not preclude limb salvage surgery in a majority of patients, did not increase the risk of local recurrence, but was associated with poorer overall survival.

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 score0.022
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.335
Teacher spread0.308 · 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

Citations52
Published2010
Admission routes2
Has abstractyes

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