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Record W4368617944 · doi:10.1097/oi9.0000000000000249

Great debates in trauma biomechanics

2023· review· en· W4368617944 on OpenAlexaff
Vaida Glatt, Robert V. O’Toole, Samir Mehta, Utku Kandemir, William M. Ricci, Aaron Nauth, Emil H. Schemitsch, Michael W. Hast

Bibliographic record

VenueOTA International The Open Access Journal of Orthopaedic Trauma · 2023
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsBiomechanicsPhysical medicine and rehabilitationPsychologyMedicineAnatomy

Abstract

fetched live from OpenAlex

At the 2021 annual meeting of the Orthopaedic Trauma Association, the Basic Science Focus Forum hosted its first ever debate-style symposium focused on biomechanics and fracture repair. The 3 subjects of debate were "Mechanics versus Biology-Which is 'More Important' to Consider?" "Locked Plate versus Forward Dynamization versus Reverse Dynamization-Which Way Should I Go?" and "Sawbones versus Cadaver Models-What Should I Believe Most?" These debates were held because fracture healing is a highly organized synergistic response between biological factors and the local mechanical environment. Multiple studies have demonstrated that both factors play roles in governing bone healing responses, and the causal relationships between the 2 remain unclear. The lack of clarity in this space has led to a spectrum of research with the common goal of helping surgeons make good decisions. Before reading further, the reader should understand that the questions posed in the debate titles are unanswerable and might represent a false choice. Instead, the reader should appreciate that the debates were held to gain a more thorough understanding of these topics based on the current state of the art of experimental and clinical studies, by using an engaging and thought-provoking format.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0000.001
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 head0.204
GPT teacher head0.479
Teacher spread0.275 · 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 designOther design
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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOTA International The Open Access Journal of Orthopaedic TraumaSame topicBone fractures and treatmentsFrench-language works237,207