MétaCan
Menu
Back to cohort
Record W2148345311 · doi:10.2217/ahe.09.30

Management of Long-Bone Metastases: A Surgical Perspective

2009· article· en· W2148345311 on OpenAlexaff
Ahmed Abdullah Alghamdi, Ingrid Hings, Robert Turcotte

Bibliographic record

VenueAging Health · 2009
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsMontreal General Hospital
Fundersnot available
KeywordsMedicineIntensive care medicinePopulationLife expectancyQuality of life (healthcare)DiseaseCancerSurgeryGeneral surgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Bone metastases are frequently encountered in the management of cancer. The incidence of such events is increasing in the geriatric population and most often they can be managed conservatively. Surgery is sometimes unavoidable to address some of these lesions and will represent a significant challenge for both the patient and the medical team. Patients presenting with bone metastases are a heterogeneous group. Some present late in the course of their disease after failing all treatment modalities whereas others present without a known diagnosis of cancer. In addition, some metastatic cancers are responsive to treatment and prolonged survival may be expected (e.g., myeloma, breast prostate, kidney and thyroid) as compared with others where therapeutic options are limited with concomitant decrease in life expectancy (e.g., lung, bladder and pancreas). Patients will benefit if physicians can recognize lesions at risk of fracture or already fractured, and understand the advantages and the limitations of surgery. Patient selection and the type of procedure performed are of outmost importance. Chances for a satisfactory functional outcome and survival rates following surgery should be known in order to avoid unnecessary procedures that may be associated with significant rates of complication. Appropriate surgical intervention for bone metastases can provide the elderly cancer patient with meaningful palliation and contribute to their overall quality of life.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.367
Teacher spread0.343 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAging HealthSame topicManagement of metastatic bone diseaseFrench-language works237,207