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Record W2020003331 · doi:10.1097/bot.0b013e31829efcc5

Radiographic Displacement in Pelvic Ring Disruption

2013· article· en· W2020003331 on OpenAlexaff
Kelly A. Lefaivre, Piotr A. Blachut, Adam J. Starr, Gerard P. Slobogean, Peter J. O’Brien

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2013
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineRadiographyDisplacement (psychology)Pelvic girdleAnatomyRadiologyNuclear medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The literature on pelvic ring disruptions is based largely on nonstandardized and nonvalidated radiographic outcomes. A thorough review of the literature revealed only 3 described methods for measuring radiographic displacement and 1 frequently used grading system for displacement. We aimed to test the reliability of these previously published radiographic measurement methods and grading system. METHODS: Five separate observers measured radiographic displacement on the standardized pre- and postoperative anteroposterior, inlet, and outlet views of 25 patients with surgically treated Tile B and C pelvic fractures. The readers measured their initial impression based on the Tornetta and Matta grading system (excellent, good, fair, and poor). Next, they measured displacement using the inlet and outlet ratio as described by Sagi, the cross measurement technique as described by Keshishyan, and the absolute displacement method (ADM) as described by Lefaivre. The millimeter measurement obtained by the ADM was converted using the Tornetta and Matta grading system. Each continuous measure was compared for interobserver reliability using intraclass correlations (ICCs), and the categorical outcomes were compared using a kappa statistic. Finally, the relationship of the initial impression to the grade as determined by the ADM was compared using kappa agreement. RESULTS: The agreement among observers based on initial impression was poor (kappa statistic, 0.306) but was fair among those reductions that were excellent (κ = 0.495). Using the Sagi method, the reliability ICC was moderate for the postoperative inlet [0.515, 95% confidence interval (CI), 0.338-0.702] and outlet ratio (0.594, 95% CI, 0.423-0.760) but almost perfect in preoperative radiographs (inlet: 0.814, 95% CI, 0.703-0.901; outlet: 0.863, 95% CI, 0.775-0.929). The ICCs for all interpretations of the Keshishyan technique were excellent but were highest when considered as a ratio (preoperative: 0.938, 95% CI, 0.894-0.969; postoperative: 0.912, 95% CI, 0.850-0.955). Using the ADM, the location and film used for measurement had poor agreement, and the ICC for the measurement in millimeters was moderate (preoperative: 0.522, 95% CI, 0.342-0.708; postoperative: 0.432, 95% CI, 0.255-0.634) and the kappa agreement poor when converted using the Tornetta and Matta scale (κ = 0.2190). The agreement between the impression and the converted grade from the ADM was poor (κ = 0.2520). CONCLUSIONS: Radiographic measurement in pelvic x-rays to date has been nonvalidated, and we found the interobserver reliability on common methods, including overall impression and absolute displacement in millimeters, to be poor. The inlet/outlet ratio as described by Sagi was reliable only with wide displacement. The cross measurement technique allows least observer choice and had excellent reliability but does not give a measurement that we can easily interpret based on convention in pelvic fracture description.

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)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 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

Citations76
Published2013
Admission routes1
Has abstractyes

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