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Enregistrement W2934653001 · doi:10.1177/2325967119s00189

PEDIATRIC ACL RECONSTRUCTION: DOES THE FEMORAL PEEK IMPLANT CAUSE TUNNEL WIDENING?

2019· article· en· W2934653001 sur OpenAlexaff
Lydia Saad, Guy Grimard, Marie‐Lyne Nault

Notice bibliographique

RevueOrthopaedic Journal of Sports Medicine · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueKnee injuries and reconstruction techniques
Établissements canadiensCentre Hospitalier Universitaire Sainte-Justine
Organismes subventionnairesnon disponible
Mots-clésMedicineAnterior cruciate ligament reconstructionPeekOrthopedic surgeryImplantAnterior cruciate ligamentFibrous jointSurgery

Résumé

récupéré en direct d'OpenAlex

Background: Because of their strength, inertness and biocompatibility, polyetherketone (PEEK) implants have been increasingly used in orthopedics, especially trauma and spine surgeries. Their mechanical and chemical properties have been widely studied and compared to other materials since their accelerated development in the 1980s. Their use is relatively new in anterior cruciate ligament reconstructions (ACLR), and there is little literature to document their effect on tunnel widening in these procedures. Bone tunnel enlargement following ACLR is a widely reported phenomenon that has been negatively correlated with clinical outcome scores. A recent prospective study reiterated its stronger association with suture discs compared with methods using interference screws, cross-pins and buttons. There is only one paper that specifically reports the results of a study on PEEK implants in relation with tunnel widening and it did not demonstrate significant association. PEEK polymer has not yet been studied in pediatric knees, in which tunnel widening has more than the usual residual laxity and re-tear implications, as it can also cause physeal damage. The purpose of this study was to investigate the use of a femoral PEEK implant in ACLR performed on skeletally immature patients and to determine if it is associated with tunnel widening. As a secondary objective, this study aimed to assess the risk of growth complications associated with the use of PEEK. Methods: All patients who underwent all-epiphyseal ACLR surgery in a pediatric university hospital between March 2015 and January 2017 were included in this retrospective study. The all-epiphyseal ACL reconstruction procedures were done using a new instrumentation system that includes a titanium tibial anchor fixation and a femoral PEEK screw system. Femoral bone tunnel diameters were measured on postoperative lateral knee radiographs. The widest tunnel measurements were taken using the sclerotic tunnel margins as reference points and compared to the known sizes of the drill bits retrieved from operative protocols. Paired t tests were performed to assess bone tunnel widening. P-values <0.05 were considered significant. SPSS 25.0 was used for statistical analysis. Results: Eighteen (19 knees) arthroscopic ACL reconstruction patients were included. Of the 18 all-epiphyseal ACL reconstruction patients, 4 (22.2%) were female and 14 (77.8%) were male. The chronological and bone ages at time of surgery (mean ± SD) were respectively 13.5 ± 1.6 and 13.3 ± 1.0 years. At a mean follow-up of 17.1 months, average tunnel enlargement was 1.8± 1.4 (0.0-4.2) mm at the femur and was found to be statistically significant (P<0.001). There were no symptomatic growth abnormalities requiring intervention, but 2 unilateral early physeal closures at the distal femur (10.5%) were noted. These patients had notable femoral tunnel enlargement. One had a 3.0 mm of femoral tunnel widening and no observable growth disturbance. The other had an asymptomatic and non-progressive unilateral knee valgum of 5 degrees and 3.5 mm of femoral tunnel widening. Conclusions/significance: This study showed that the largest tunnel increase at a mean follow up of 17.1 months was 4.2 mm. It is not clear that this widening is clinically significant even though it is statistically significant. Also, association between femoral tunnel widening and physeal closure could not be formally established. Thus, further research on this potential risk factor is needed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,254
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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