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Record W2411107431 · doi:10.1097/bot.0000000000000390

Determination of Radiographic Healing

2015· article· en· W2411107431 on OpenAlexaff
Jody Litrenta, Paul Tornetta, Samir Mehta, Clifford B. Jones, Robert V. O’Toole, Mohit Bhandari, Stephen Kottmeier, Robert F. Ostrum, Kenneth A. Egol, William M. Ricci, Emil H. Schemitsch, Daniel S. Horwitz

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2015
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsSt. Michael's HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineRadiographyIntramedullary rodBone healingOrthopedic surgeryFemurIntraclass correlationTibiaSurgeryConfidence intervalOrthodonticsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the reliability of the Radiographic Union Scale for Tibia (RUST) score and a new modified RUST score in quantifying healing and to define a value for radiographic union in a large series of metadiaphyseal fractures treated with plates or intramedullary nails. DESIGN: Healing was evaluated using 2 methods: (1) evaluation of interrater agreement in a series of radiographs and (2) analysis of prospectively gathered data from 2 previous large multicenter trials to define thresholds for radiographic union. INTERVENTION: Part 1: 12 orthopedic trauma surgeons evaluated a series of radiographs of 27 distal femur fractures treated with either plate or retrograde nail fixation at various stages of healing in random order using a modified RUST score. For each radiographic set, the reviewer indicated if the fracture was radiographically healed. Part 2: The radiographic results of 2 multicenter randomized trials comparing plate versus nail fixation of 81 distal femur and 46 proximal tibia fractures were reviewed. Orthopaedic surgeons at 24 trauma centers scored radiographs at 3, 6, and 12 months postoperatively using the modified RUST score above. Additionally, investigators indicated if the fracture was healed or not healed. MAIN OUTCOME MEASURES: The intraclass correlation coefficient (ICC) with 95% confidence intervals was determined for each cortex, the standard and modified RUST score, and the assignment of union for part 1 data. The RUST and modified RUST that defined "union" were determined for both parts of the study. RESULTS: ICC: The modified RUST score demonstrated slightly higher ICCs than the standard RUST (0.68 vs. 0.63). Nails had substantial agreement, whereas plates had moderate agreement using both modified and standard RUST (0.74 and 0.67 vs. 0.59 and 0.53). UNION: The average standard and modified RUST at union among all fractures was 8.5 and 11.4. Nails had higher standard and modified RUST scores than plates at union. The ICC for union was 0.53 (nails: 0.58; plates: 0.51), which indicates moderate agreement. However, the majority of reviewers assigned union for a standard RUST of 9 and a modified RUST of 11, and >90% considered a score of 10 on the RUST and 13 on the modified RUST united. CONCLUSIONS: The ICC for the modified RUST is slightly higher than the standard RUST in metadiaphyseal fractures and had substantial agreement. The ICC for the assessment of union was moderate agreement; however, definite union would be 10 and 13 with over 90% of reviewers assigning union. These are the first data-driven estimates of radiographic union for these scores.

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)0.005
metaresearch head score (Gemma)0.017
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: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.036
GPT teacher head0.305
Teacher spread0.269 · 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
GenreMethods

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

Citations274
Published2015
Admission routes1
Has abstractyes

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