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Record W4385563761 · doi:10.1002/jso.27409

Emerging innovations and advancements in the field of musculoskeletal oncology

2023· article· en· W4385563761 on OpenAlexaff
R. Lor Randall, Raminta Theriault, Steven W. Thorpe, Michael J. Monument

Bibliographic record

VenueJournal of Surgical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSubspecialtyMedicineHealth careSocioeconomic statusMEDLINEPopulationMedical educationInternal medicineFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Orthopaedic oncology has evolved dramatically as a discipline over the past 50 years.[1][2][3][4] With the advent of subspecialty fellowship training and transdisciplinary collaboration, our field has pushed the intellectual boundaries on several academic fronts, some of which we will highlight in this special edition of JSO.Broadly, we have chosen to focus on (1) outcomes, disparities and quality of life; (2) surgical management and technological developments; and (3) nonsurgical advancements.These areas are helping to redefine the standard of care.We hope the reader will enjoy this update on these salient, dynamic aspects of orthopaedic oncology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.003
Scholarly communication0.0060.008
Open science0.0020.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0130.003

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 head0.030
GPT teacher head0.418
Teacher spread0.388 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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