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Record W2409966118 · doi:10.2106/jbjs.o.00722

Devastating Impact of Fracture Nonunions: The Need for Timely Identification and Intervention for High-Risk Patients

2015· article· en· W2409966118 on OpenAlexaff
Raman Mundi, Mohit Bhandari

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsHamilton Health SciencesHamilton General HospitalMcMaster University
Fundersnot available
KeywordsIntervention (counseling)Identification (biology)MedicineFracture (geology)Intensive care medicineEngineeringNursing

Abstract

fetched live from OpenAlex

The Journal publishes corrections when they are of significance to patient care, scientific data or record-keeping, or authorship, whether that error was made by an author, editor, or staff. Errata also appear in the online version and are attached to files downloaded from jbjs.org. In the article entitled “Factors Affecting Length of Stay, Readmission, and Revision After Shoulder Arthroplasty: A Population-Based Study” (2015 Aug 5;97[15]:), two footnotes in Table II on page 1261 were incorrectly transposed and one table entry on page 1260 had the incorrect footnote symbol. Specifically, “†Cutpoints defining the five categories for facility volume and surgeon volume are the 10th, 50th, 75th, and 90th percentiles of the counts of procedures in the year prior to the index arthroplasty. This approach was chosen as a practical strategy for a categorical parameterization of likely nonlinear relationships of these factors with the three outcomes. ‡The indicators for the diagnoses and the specific comorbidities do not define mutually exclusive categories as patients can have multiple diagnoses and comorbidities. All of the other categorical factors in this table (sex, age, race, insurance type, Charlson Comorbidity Index, facility and surgeon case volumes) are coded with mutually exclusive levels.” should have read “†The indicators for the diagnoses and the specific comorbidities do not define mutually exclusive categories as patients can have multiple diagnoses and comorbidities. All of the other categorical factors in this table (sex, age, race, insurance type, Charlson Comorbidity Index, facility and surgeon case volumes) are coded with mutually exclusive levels. ‡Cutpoints defining the five categories for facility volume and surgeon volume are the 10th, 50th, 75th, and 90th percentiles of the counts of procedures in the year prior to the index arthroplasty. This approach was chosen as a practical strategy for a categorical parameterization of likely nonlinear relationships of these factors with the three outcomes.” and “Specific comorbidities‡” should have read as “Specific comorbidities†”.

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)0.001
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 score0.207
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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