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Record W2502690185

BIOMECHANICAL STUDIES IN ORTHOPAEDIC SURGERY: SCIENCE (F)OR COMMON SENSE?

2014· article· en· W2502690185 on OpenAlexaboutno aff
Jos J. Mellema, Job N. Doornberg, T. Quitton, David Ring

Bibliographic record

VenueJournal of Bone and Joint Surgery-british Volume · 2014
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Fixation (population genetics)Construct validityMedicinePsychologyPhysical therapySurgeryComputer sciencePatient satisfaction
DOInot available

Abstract

fetched live from OpenAlex

Summary Biomechanical studies comparing fixation constructs are predictable and do not relate to the significant clinical problems. We believe there is a need for more careful use of resources in the lab and better collaboration with surgeons to enhance clinical relevance. Introduction It is our impression that many biomechanical studies invest substantial resources studying the obvious: that open reduction and internal fixation with more and larger metal is stronger. Studies that investigate “which construct is the strongest?” are distracted from the more clinically important question of “how strong is strong enough?”. The aim of this study is to show that specific biomechanical questions do not require formal testing. This study tested our hypothesis that the outcome of a subset of peer reviewed biomechanical studies comparing fracture fixation constructs can be predicted based on common sense with great accuracy and good interobserver reliability. Patients & Methods Between 2000 and 2012, we found 254 peer reviewed biomechanical studies in prestigious orthopaedic journals comparing construct ‘A’ versus construct ‘B’ to evaluate load to failure in order to determine ‘which construct is the strongest?’. Eleven studies comparing fracture fixation constructs were randomly selected from different journals based on our sense that the answer was obvious prior to performing the study. Three-hundred independent observers; including orthopaedic- and general- surgeons affiliated with the Science of Variation Group (www.scienceofvariation.org), predicted the outcome of these biomechanical studies. Observers were presented the original published illustrations of different treatment modalities and were asked to answer one question: “which construct is the strongest?” Sensitivity, specificity and accuracy were calculated according to standardised formulas. The agreement among the observers was calculated by using a multirater kappa, described by Siegel and Castellan. The kappa values were interpreted as proposed by Landis and Koch. Results Accuracy was the same or greater than 80% for all studies except for study 1. The level of experience had no influence on the accuracy of predicting outcomes. Sensitivity averaged 84%, ranging from 60% (for study 1) to 99% (for study 7), specificity 86%, ranging from 60% (for study 1) to 99% (for study 7), and accuracy averaged 86% from 60% (for study 1) to 99% (for study 7). The overall categorical rating of inter-observer reliability according to Landis and Koch was moderate (κ = 0,53; SE = 0.01), ranging from κ = 0,03 (SE = 0.01) to κ = 0,95 (SE = 0.01). Analyses of SOVG subgroups identified excellent agreement among Canadian surgeons. Moderate and substantial agreement were found in most of other subgroups: ranging from first year medical students to specialists 20 years or more in practice; and specialists who practice in Australia, Europe and United States. Study 5 was easiest to predict based on common sense (Accuracy 97%, inter-observer reliability 0,88). Study 1 was predicted with least accuracy 61% and the lowest kappa value 0,04. Conclusions The outcomes of biomechanical studies comparing fracture reduction and fixation constructs are highly predictable with good inter-observer reliability.

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)0.311
metaresearch head score (Gemma)0.605
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.689
Threshold uncertainty score0.850

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3110.605
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0210.015
Science and technology studies0.0030.045
Scholarly communication0.0170.020
Open science0.0040.010
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.052
GPT teacher head0.308
Teacher spread0.256 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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