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Record W4313198883 · doi:10.1136/emermed-2022-rcem2.47

1632 Clinical predictors of fracture in patients with shoulder dislocation: systematic review of diagnostic test accuracy studies

2022· article· en· W4313198883 on OpenAlexaboutno aff
Ilaria Oldrini, Laura Coventry, Alex Novak, Steve Gwilym, David Metcalfe

Bibliographic record

VenueEmergency Medicine Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConcomitantReduction (mathematics)EcchymosisRadiographyEmergency departmentLogistic regressionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Aims, Objectives and Background Pre-reduction radiographs are conventionally used to exclude important fracture before attempts to reduce a dislocated shoulder in the Emergency Department. However, this step increases cost, exposes patients to ionising radiation, and might delay closed reduction. Some studies have suggested that pre-reduction imaging may be omitted for a sub-group of patients with shoulder dislocations. The objective was to determine whether clinical predictors can identify patients that might safely undergo closed reduction of a dislocated shoulder without pre-reduction radiographs. Method and Design A systematic review and meta-analysis of diagnostic test accuracy studies that have evaluated the ability of clinical features to identify concomitant fractures in patients with shoulder dislocation. All fractures were included except for Hill-Sachs lesions. Quality assessment was undertaken using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Data were pooled and meta-analysed by fitting univariate random effects and multi-level mixed effects logistic regression models. Results and Conclusion Eight studies reported data on 2,087 shoulder dislocations and 343 concomitant fractures. The prevalence of concomitant fracture was 17.5%. The most accurate clinical predictors were age >40 (LR+ 1.8 [95% CI 1.5–2.1]; LR- 0.4 [0.2–0.6]), female sex (LR+ 2.0 [1.6–2.4], LR- 0.7 [0.6–0.8]), first time dislocation (LR+ 1.7 [1.4–2.0]; LR-0.2 [0.1–0.5]), and presence of humeral ecchymosis (LR+ 3.0–5.7; LR- 0.8–1.1). The most important mechanisms of injury were: high-energy mechanism fall (LR+ 2.0–9.8), fall >1 flight of stairs (LR+ 3.8 [95% CI 0.6–13.1]; LR- 1.0 [95% CI 0.9–1.0]), and motor vehicle collision (LR+ 2.3 [0.5–4.0]; LR- 0.9 [0.9–1.0]). The Quebec Rule had a sensitivity of 92.2% (95% CI 54.6–99.2%) and specificity (33.3%, 23.1–45.3%) but the Fresno-Quebec rule maintained 100% sensitivity across three studies that included 564 shoulder dislocations and 98 fractures. In conclusion, the Fresno-Quebec Rule has undergone both internal and external validation and may now have a role in clinical practice.

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)0.002
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.038
GPT teacher head0.433
Teacher spread0.395 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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