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Record W7106342662 · doi:10.48620/92523

The epidemiology of scaphoid fractures and non-unions in Switzerland: a nationwide analysis of the socioeconomic impact.

2025· article· en· W7106342662 on OpenAlexaff

Bibliographic record

VenueOpen Access CRIS of the University of Bern · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsHand and Upper Limb Clinic
Fundersnot available
KeywordsEpidemiologySocioeconomic statusWristIncidence (geometry)Scaphoid boneScaphoid fractureRisk factor

Abstract

fetched live from OpenAlex

Scaphoid fractures are the second most common wrist injury after distal radius fractures and primarily affect young individuals in their most productive working years. Some scaphoid fractures fail to heal, potentially resulting in chronic pain, functional impairment, and long-term osteoarthritis in the wrist, which may require salvage procedures and can have a significant impact on work capacity. Considerable debate exists around the biological and injury-related risk factors for scaphoid non-unions, as well as the criteria for surgical intervention. However, epidemiological data are still limited, often derived from small populations, and incidence reports about scaphoid non-unions remain inconsistent. In a retrospective analysis of over 9 Mio injuries, recorded by the Swiss National Accident Insurance (Suva) Statistical Service from 2008 to 2021, we identified 16,691 scaphoid fractures. The male-to-female ratio was 4:1 for both scaphoid fractures and non-unions. The rate of primary surgery was 1:3. Around 14% of scaphoid fractures progressed to non-union, and 3% developed posttraumatic arthritis. Patients with non-unions were three times more likely to be unable to work for more than 12 months. Blue-collar workers were particularly at risk for a non-union and for extended work absence. Notably, the type of treatment (surgical or non-surgical) had no significant effect on work absence-non-union itself was the key determinant.

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)0.000
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 score0.007
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.018
GPT teacher head0.363
Teacher spread0.345 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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