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Record W2068782484 · doi:10.1097/blo.0b013e31803ea9c8

Outcomes of an Anatomically Based Approach to Metastatic Disease of the Acetabulum

2007· article· en· W2068782484 on OpenAlexaff
Michelle Ghert, Khalid Alsaleh, Forough Farrokhyar, Nigel Colterjohn

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2007
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineAcetabulumSurgery

Abstract

fetched live from OpenAlex

Metastatic disease of the acetabulum is a common and challenging surgical problem. We asked whether acetabular reconstruction for metastatic bone disease improves functional outcome with an acceptable risk of surgical morbidity. We also asked if primary tumor type and the presence of visceral metastases predicted patient survival. We analyzed prospectively accumulated records of 62 consecutive patients who underwent 63 hip arthroplasties with acetabular reconstruction. Operative technique was guided by the extent of dome and column involvement. Demographics, functional status in the form of the Eastern Cooperative Oncology Group (ECOG) score, and survival data were analyzed. Functional scores improved from an average of 2.6 preoperatively to 1.1 postoperatively. Four patients had postoperative complications for which we performed further surgery. Mean survival for the patients with breast cancer was longer at 21 months compared to 9 months for the patients with other primary malignancies. Patients who did not present with visceral metastases had longer survival than those with visceral metastases. Despite the moderate risk of operative complications, an anatomically based approach to reconstruction of acetabular defects from metastatic disease improves functional outcome. Breast cancer as the primary malignancy and the absence of visceral metastases predicted longer 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)0.012
metaresearch head score (Gemma)0.007
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 score0.042
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.109
GPT teacher head0.459
Teacher spread0.350 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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