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Olecranon fractures

2023· review· en· W4318754413 on OpenAlexaff
Andrew D. Duckworth, Thomas H. Carter, Michael Chen, Michael J. Gardner, Adam C. Watts

Bibliographic record

VenueThe Bone & Joint Journal · 2023
Typereview
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsCentre for Global Health Research
FundersNational Institute for Health and Care Research
KeywordsOlecranonMedicineGeologyComputer scienceAnatomyElbow

Abstract

fetched live from OpenAlex

Despite being one of the most common injuries around the elbow, the optimal treatment of olecranon fractures is far from established and stimulates debate among both general orthopaedic trauma surgeons and upper limb specialists. It is almost universally accepted that stable non-displaced fractures can be safely treated nonoperatively with minimal specialist input. Internal fixation is recommended for the vast majority of displaced fractures, with a range of techniques and implants to choose from. However, there is concern regarding the complication rates, largely related to symptomatic metalwork resulting in high rates of implant removal. As the number of elderly patients sustaining these injuries increases, we are becoming more aware of the issues associated with fixation in osteoporotic bone and the often fragile soft-tissue envelope in this group. Given this, there is evidence to support an increasing role for nonoperative management in this high-risk demographic group, even in those presenting with displaced and/or multifragmentary fracture patterns. This review summarizes the available literature to date, focusing predominantly on the management techniques and available implants for stable fractures of the olecranon. It also offers some insights into the potential avenues for future research, in the hope of addressing some of the pertinent questions that remain unanswered. Cite this article: Bone Joint J 2023;105-B(2):112–123.

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 categoriesResearch integrity, 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 score0.978
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.171
GPT teacher head0.407
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.

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

Citations26
Published2023
Admission routes1
Has abstractyes

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