MétaCan
Menu
Back to cohort
Record W4392954422 · doi:10.1503/cjs.007822

Position statement: management of proximal humerus fractures

2024· article· en· W4392954422 on OpenAlexaffvenue
Peter Lapner, Ujash Sheth, Diane Nam, Emil H. Schemitsch, Pierre Guy, Robin Richards

Bibliographic record

VenueCanadian Journal of Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsVancouver General HospitalHealth Sciences CentreOttawa HospitalVancouver Hospital and Health Sciences CentreUniversity of TorontoUniversity of OttawaUniversity of British ColumbiaSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsMedicineMEDLINERandomized controlled trialIntramedullary rodInternal fixationSurgeryPhysical therapyEvidence-based medicine

Abstract

fetched live from OpenAlex

We sought to compare outcomes and reoperation rates for the surgical treatment of proximal humerus fractures (excluding head-splitting fractures, fracture-dislocations, and isolated greater-tuberosity fractures) in men and women older than 60 years. We searched MEDLINE, Embase, and Cochrane through to Feb. 1, 2022, and included all English-language randomized trials comparing operative versus nonoperative treatment; open reduction and internal fixation (ORIF) with locking plate versus intramedullary nail; arthroplasty versus ORIF; and reverse shoulder arthroplasty versus hemiarthroplasty. Outcomes of interest were functional outcomes (e.g., Constant score), pain outcomes (visual analogue scale scores), and reoperation rates for the interventions of interest when available. We rated the quality of the evidence and strength of recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. This guideline will benefit patients considering surgical intervention for fractures of the proximal humerus by improving counselling on surgical treatment options and possible outcomes. It will also benefit surgical providers by improving their knowledge of various surgical approaches. Data presented could be used to develop frameworks and tools for shared decision-making. Nous avons cherché à comparer les résultats et les taux de réintervention à la suite d’un traitement chirurgical pour une fracture de l’humérus proximal (excluant les fractures de la tête humérale, les fractures-luxations et les fractures isolées de la grande tubérosité) chez les hommes et les femmes âgés de plus de 60 ans. Nous avons effectué des recherches dans les bases de données MEDLINE, Embase, et Cochrane jusqu’au 1er février 2022 et avons inclus tous les essais randomisés publiés en anglais comparant différents duos d’interventions : traitements chirurgicaux ou non chirurgicaux; réductions ouvertes avec fixation interne (ROFI) réalisées à l’aide d’une plaque verrouillée ou enclouages centromédullaires; arthroplasties ou ROFI; et arthroplasties inversées de l’épaule ou hémiarthroplasties. Les paramètres d’intérêt étaient la capacité fonctionnelle (p. ex., score de Constant), la douleur (p. ex., échelle analogique visuelle) et le taux de réintervention pour les interventions d’intérêt, selon les données disponibles. Nous avons évalué la qualité des données probantes et la solidité des recommandations à l’aide de l’approche GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Cette ligne directrice profitera aux patients qui envisagent une intervention chirurgicale après une fracture de l’humérus proximal en améliorant les consultations sur les options de traitement chirurgical et les résultats escomptés. Elle aidera aussi les chirurgiens en améliorant leurs connaissances sur différentes approches chirurgicales. Les données présentées pourraient servir à mettre au point des cadres et des outils pour une prise de décision partagée.

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)0.021
metaresearch head score (Gemma)0.086
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.086
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.004
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0050.003
Research integrity0.0180.008
Insufficient payload (model declined to judge)0.0240.015

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 head0.315
Teacher spread0.279 · 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
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of SurgerySame topicShoulder Injury and TreatmentFrench-language works237,207