Improved precision of radiographic measurements for distal radius fractures after a technique-teaching tutorial
Bibliographic record
Abstract
Improved precision of radiographic measurements for distal radius fractures after a techniqueteaching tutorialBackground: For the management of distal radius fractures, surgical decision-making depends on radiographic measurements of indicators including radial inclination (RI), ulnar variance (UV) and radial tilt (RT).Evaluation of the inter-and intrarater reliability of surgeons' measurements of these criteria has been limited.Methods: Twelve physicians were invited to participate in this study.Anonymously, they measured RI, UV and RT on 30 digitally stored radiographs of distal radius fractures on 3 occasions, each at least 1 week apart, using online measuring tools.After taking the third set of measurements, the participants were given a tutorial by the senior author (G.J.) on a single technique to measure all 3 indicators.The partici pants then took 3 more sets of measurements using only the technique they had been taught.Intraclass correlation coefficients (ICCs) were used to evaluate interrater reliability each week.Multiple logistic regression was used to calculate the effect of the tutorial, controlling for week of study along with reader (participant) and patient variance.Results: The ICCs indicated that the participants' measurement precision improved promptly after the tutorial, and this improvement was sustained through subsequent readings.The odds of an "accurate" measurement (within 2° of the senior author's measurements for RI, 1 mm for UV and 4° for RT) was 1.7 times higher for RI, 2.7 times higher for UV and 2.3 times higher for RT after the tutorial; all of these results were statistically significant.Conclusion: Surgeons ought to be familiar with a method to reproducibly measure the indicators used in the published guidelines for surgical intervention.The tutorial on a single standardized technique for online measurement of RI, UV and RT in distal radius fractures improved measurement precision.Contexte : Pour la prise en charge des fractures du radius distal, la prise de décisions chirurgicales dépend de la mesure de plusieurs indicateurs sur les images radiographiques : l'inclinaison radiale (IR), la variance ulnaire (VU) et l'inclinaison sagittale du radius (ISR).La fiabilité interévaluateurs et intra-évaluateur des mesures de ces critères par les chirurgiens a été peu étudiée.Méthodes : Nous avons invité 12 médecins à participer à l'étude.En tout anonymat, ils ont déterminé l'IR, la VU et l'ISR au moyen d'outils de mesure en ligne sur 30 radiographies numérisées de fractures du radius distal.Ils ont répété l'exercice à 3 reprises, à au moins 1 semaine d'intervalle.Après la troisième série, les participants ont suivi un tutoriel de l'auteur principal (G.J.) sur une technique qui peut à elle seule mesurer les 3 indicateurs.Les participants ont ensuite fait 3 autres séries de mesures en utilisant seulement cette technique.Nous avons évalué la fiabilité interévaluateurs pour chaque semaine à partir des coefficients de corrélation intraclasse (CCI).De plus, nous avons calculé l'effet du tutoriel par régression logistique multiple, en tenant compte de la semaine de l'étude et de la variation selon les lecteurs (participants) et les patients.Résultats : Les CCI indiquent que la précision des mesures s'est améliorée rapidement après le tutoriel; cette amélioration a d'ailleurs persisté tout au long des séries subséquentes.La probabilité d'une mesure « exacte » (dont l'écart par rapport aux mesures de l'auteur principal est inférieur à 2° pour l'IR, à 1 mm pour la VU et à 4° pour l'ISR) était 1,7 fois plus grande pour l'IR, 2,7 fois plus grande pour la VU et 2,3 fois plus grande pour l'ISR après le tutoriel.Tous ces résultats sont statistiquement significatifs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".