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Record W4415571143 · doi:10.1302/1358-992x.2025.11.031

TIBIAL PLATEAU FRACTURES: INTER-RATER RELIABILITY FOR SURGICAL DECISION MAKING

2025· article· en· W4415571143 on OpenAlexaboutno aff
J. A. Campbell, Anders Erik Astrup Dahm, David Stephen, Sebastian Tomescu, David Wasserstein, Richard Jenkinson

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsOrthopedic surgeryReliability (semiconductor)CondyleDepression (economics)Tibial plateau fracturePlateau (mathematics)

Abstract

fetched live from OpenAlex

Tibial plateau fractures can result in significant disability and post-traumatic arthritis. They are typically classified using the AO/OTA and Schatzker classifications; however, there is a lack of evidence to define cut-off indications for surgical repair based on joint depression and/or stability (Schatzker I, II, III or AO 41.B1,2,3). The aim of the study was to determine surgeon agreement for surgical indications in a series of real cases deemed ‘borderline’ and to define the most important factors involved in surgical decision making. A survey was designed that included six tibial plateau fracture cases (Schatzker I, II, III, AO Type 41.B1,2,3). These were cases generally deemed borderline for operative management by the senior authors, with depression < 6 mm and condylar widening < 5 mm, and no significant comminution. The survey was sent out through the Canadian Orthopaedic Association (COA), Canadian Orthopaedic Trauma Society (COTS), and Austrian Society of Trauma Surgery (ÖGU). Participants were asked to review x-rays and CT scans as well as a brief clinical history for each patient and then select to offer either surgical or non-surgical management. Questions were then asked to identify and rank factors influencing the decision (fracture pattern, depression, activity level, age, osteoarthritis, comorbidities, malalignment, and instability). Data was collected through Survey Monkey. Data analysis included participant completion rate, demographic data and agreement on surgical indications. Rank sum analysis was performed for most important factors for decision making. The clinical features of cases with high and low treatment agreement were analysed further with a specific emphasis on joint depression. 189 orthopedic surgeons, fellows, and residents responded to the survey. The completion rate was 64% (70% staff, 30% trainees). There were minimal differences between the responses of trainees and staff. Three cases had high agreement (>80%) for either operative or non-operative treatment. The most frequently reported factor in decision making was fracture pattern (86%; rank value 2.0), followed by amount of depression (79%; rank value 2.1) and age (56%). Co-morbidity was the third highest ranked factor. Depression was 5mm and 7mm for the cases offered surgery. Three cases had low agreement (∼50%) for either operative or non-operative treatment. The most frequently reported factors were amount of depression (82%; rank 1.8), followed by fracture pattern (73%; rank 2.0) and age (60%). Absence or presence of knee instability was also deemed important. Depression was ∼4mm for two of the cases and 2mm for the other. The agreement among Orthopaedic Surgeons to operate on a series of ‘borderline’ Type I-III Schatzker tibial plateau fractures ranged between 51 and 99%. Cases with both high and low agreement cited fracture pattern and amount of depression as critically important. This suggests that while surgeons believe those factors are important in their decision-making process they do not uniformly agree on the parameters. The least agreement revolved around cases with depression around 4mm. These results suggest that further research is needed to objectively define the benefits (or lack thereof) of surgical treatment within these common subtypes of fractures.

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)0.186
metaresearch head score (Gemma)0.265
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 score0.186
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1860.265
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.318
Teacher spread0.308 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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