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Record W4237413876 · doi:10.1177/2325967119s00302

Comparing the Efficacy of Kneeling Stress Radiographs and Weighted Gravity Stress Radiographs to Assess Posterior Cruciate Ligament Insufficiency

2019· article· en· W4237413876 on OpenAlexaff
Charles L. Holliday, Ryan Martin, John A. Grant

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineKneelingRadiographyOrthodonticsSupine positionTibiaPosterior cruciate ligamentStress fracturesDisplacement (psychology)Weight-bearingCondyleSurgeryAnterior cruciate ligament

Abstract

fetched live from OpenAlex

Objectives: Kneeling stress radiographs are commonly used to evaluate posterior cruciate ligament (PCL) laxity. Patients, however, report significant pain, and reproducibility is challenged due to its dependence on patient body weight distribution to produce posterior tibial displacement (PTD). Weighted gravity stress radiographs may offer better reproducibility and comfort than the kneeling technique, but its efficacy has not been studied. Hypothesis: weighted gravity radiographs will be more comfortable and produce similar PTD measurements when compared to the kneeling technique. Methods: Patients 18-70 years old with non-operatively or >6 months post-operatively treated PCL injuries (isolated or multi-ligamentous) were recruited from two academic level one trauma centers to undergo bilateral PCL stress radiographs. Exclusions: open/bilateral injuries, fractures. Patients underwent PCL stress radiographs by two randomly ordered methods. Kneeling stress views: patients knelt on padded scales (separate for each knee) with the padding distal to the tibial tubercle. Patients were verbally encouraged to place equal weight on both knees (scale outputs not visible to the patient). A digital radiography plate was placed between the legs to acquire bilateral lateral radiographs. Weighted gravity stress views: Patients lay supine with their hip and knee at 90°with the heel supported. A 20 lb weight was placed on the anterior tibia just distal to the tibial tubercle. A lateral radiograph was taken and then repeated on the contralateral leg. Images were anonymized and uploaded to PACS for measurement. Outcomes: side to side difference (SSD) in translation of the posterior tibial condyles relative to the posterior femoral condyles (primary outcome); patient-reported VAS knee pain (100 mm) during the radiographs; time required to acquire the images; patient preference for technique. Statistics: sample size = 31 patients to detect a 2 mm difference (α=0.05, power 80%, SD = 2.8 mm [Jung, 2006]). Paired t-tests were used to compare the SSD between the kneeling and weighted gravity methods, VAS pain, and time to complete the radiographs. Results: 40 patients (77.5% male, 34.5 ± 12.8yrs old, 65% left knee) were recruited. 42.5% had undergone PCL reconstruction. There was no difference between the two radiographic methods in the mean SSD (kneeling = 6.29 ± 4.58 mm, gravity = 6.82 ± 4.60 mm, p= 0.61). There was no difference in the total time required to perform the radiographs (kneeling = 307.3 s ± 140.5 s, gravity = 318.7 s ± 151.1 s, p= 0.73) or the number of radiographs taken to obtain acceptable images (kneeling = 3.6 ± 1.6, gravity = 3.7 ± 1.7, p= 0.73). The amount of weight placed on each knee during the kneeling views differed slightly but was not significant (affected = 21.5 ± 11.3 kg, unaffected = 26.1 ± 12.1 kg, p= 0.09). There was significantly less knee pain reported for the gravity views (kneeling = 31.8 ± 26.6, gravity = 4.0 ± 12.0, p < 0.0001). 94.6% of patients preferred the gravity method. Conclusion: Weighted gravity PCL stress radiographs should be considered for use in clinical practice as they produce similar posterior tibial translation values to the kneeling technique, do not rely on patient weightbearing, and provide significantly better patient comfort.

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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)0.005
metaresearch head score (Gemma)0.014
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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.274
Teacher spread0.260 · 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".

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Citations0
Published2019
Admission routes1
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