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Record W2780051905 · doi:10.1055/s-0037-1615298

Patients with Revision Modern Megaprostheses of the Distal Femur Have Improved Disease-Specific and Health-Related Outcomes Compared to Those with Primary Replacements

2017· article· en· W2780051905 on OpenAlexaboutno aff
Clémence Heyberger, Guillaume Auberger, A. Babinet, Philippe Anract, David Biau

Bibliographic record

VenueThe Journal of Knee Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFemurImplantQuality of life (healthcare)Surgery

Abstract

fetched live from OpenAlex

Abstract We asked whether there would be any difference between primary and revision modern cemented fixed hinge megaprosthesis of the distal femur in function and activity-related outcomes following treatment of a bone tumor. An identical custom-made fixed hinge cemented megaprosthesis with a hydroxyapatite collar was used in all cases. The main outcomes were joint-specific function, disease-specific activity, and health-related quality of life. Implant survival was also evaluated. Patients in the revision group performed slightly better than patients in the primary group on disease-specific (Toronto Extremity Salvage Score, p = 0.033; Musculoskeletal Tumor Society, p = 0.072) and health-related outcomes (Short Form 36 [SF-36] physical component, p = 0.085; SF-36 mental component, p = 0.069) but not on joint-specific outcomes (Knee Society Score, p = 0.94). The cumulative probabilities of revision for any reason were 14.5% (7–25%) at 5 years with no statistically significant difference between primary and revision procedures (p = 0.77). In conclusion, patients undergoing a revision have similar joint-specific functional outcome but improved disease-specific and health-related outcomes. Implant survival are similar between groups.

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 score0.026
Threshold uncertainty score0.293

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)0.0000.000

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 head0.037
GPT teacher head0.273
Teacher spread0.236 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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