MétaCan
Menu
Back to cohort
Record W3186072529 · doi:10.3390/curroncol28040238

Emerging Concepts in the Surgical Management of Peri-Acetabular Metastatic Bone Disease

2021· article· en· W3186072529 on OpenAlexaffvenue
Aaron Gazendam, Daniel Axelrod, David C. Wilson, Michelle Ghert

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePelvisSurgeryDiseaseRadiological weaponPercutaneous

Abstract

fetched live from OpenAlex

The pelvis is a common site of metastatic bone disease. Peri-acetabular lesions are particularly challenging and can cause severe pain, disability and pathologic fractures. Surgical management of these lesions has historically consisted of cementoplasty for contained lesions and Harrington reconstructions for larger, more destructive lesions. Due to the limitations of these procedures, a number of novel procedures have been developed to manage this challenging problem. Percutaneous techniques-including acetabular screw fixation and cementoplasty augmented with screws-have been developed to minimize surgical morbidity. Recent literature has demonstrated a reliable reduction in pain and improvement in function in appropriately selected patients. Several adjuncts to the Harrington procedure have been utilized in recent years to reduce complication rates. The use of constrained liners and dual mobility bearings have reduced the historically high dislocation rates. Cage constructs and porous tantalum implants are becoming increasingly common in the management of large bony defects and destructive lesions. With novel and evolving surgical techniques, surgeons are presented with a variety of surgical options to manage this challenging condition. Physicians must take into account the patients' overall health status, oncologic prognosis and anatomic location and extent of disease when developing an appropriate surgical plan.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.444
Teacher spread0.347 · 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.

Study designNot applicable
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

Citations15
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCurrent OncologySame topicManagement of metastatic bone diseaseFrench-language works237,207