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Record W2963920426 · doi:10.5435/jaaos-d-19-00112

Emerging Options for Biologic Enhancement of Stress Fracture Healing in Athletes

2019· review· en· W2963920426 on OpenAlexaff
Timothy L. Miller, Christopher C. Kaeding, Scott A. Rodeo

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2019
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineNonunionStress fracturesElite athletesAthletesIntramedullary rodBone healingPhysical therapyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

In an era of continual single-sport specialization and year-round training, overuse injuries, including stress injuries of bone, are increasingly common. These injuries can be season- or even career-ending. For many elite and professional athletes, the traditional treatment strategy of immobilization and extended rest from sports participation is often not practical or acceptable. An understanding of modern strategies for evaluating and treating stress fractures is paramount for maintaining athletic participation and optimal athletic performance. This begins with the ability to categorize and stratify bony stress injuries by both severity and risk of fracture progression. Surgical procedures such as open reduction and internal fixation or intramedullary fixation with possible bone grafting remain the standard of care for chronic or severe stress fractures. However, emerging techniques to augment the biologic environment are a minimally invasive adjunct for stimulating and supporting bone healing in elite-level athletes to optimize bone health, expedite recovery, and decrease the risk of nonunion or catastrophic fracture.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.379
Teacher spread0.327 · 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 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

Citations30
Published2019
Admission routes1
Has abstractyes

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