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Enregistrement W2800048814 · doi:10.1007/s11999.0000000000000137

CORR Insights®: Is Navigation-guided En Bloc Resection Advantageous Compared With Intralesional Curettage for Locally Aggressive Bone Tumors?

2018· letter· en· W2800048814 sur OpenAlexaboutno aff
Darin Davidson

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueBone Tumor Diagnosis and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineCurettageSurgeryFixation (population genetics)CryotherapyProsthesisFibulaTibia

Résumé

récupéré en direct d'OpenAlex

Where Are We Now? Benign but aggressive bone lesions, most commonly giant cell tumor of bone, often occur in the epiphyses and metaphyses of long bones, potentially decreasing adjacent-joint function and quality of life. In addition, these tumors can compromise bone stock (especially if they recur), making subsequent joint salvage with standard arthroplasty techniques challenging. Because the patients who get these tumors often are young, complications associated with treatment can linger for years. Most of these tumors can be treated with of curettage, sometimes combined with adjuvant treatments, such as phenol, cryotherapy, argon beam laser, and reconstruction with bone graft or cement and possibly plate fixation. Alternatively, en bloc resection of the tumor can be considered, particularly in sites which are not structurally critical such as the fibula. Though the indications have not yet been widely adopted, some advocate resection of tumors in structural sites followed by reconstruction with either allograft or a prosthesis. The advantage of curettage is that it preserves the adjacent joint, but it may increase the risk of local recurrence. By contrast, en bloc resection reduces that risk, but the reconstructions are more involved, and may be associated with more complications, both early and later on. Therefore, choosing a treatment calls for a balance between local control and functional considerations. Farfalli and colleagues compared curettage and bone graft reconstruction with navigated en bloc resection followed by fresh-frozen allograft reconstruction in patients with benign, aggressive bone lesions; they found no difference between the treatments in local recurrence, complications, or function as measured by the Musculoskeletal Tumor Society (MSTS) score [2]. Other studies on curettage and reconstruction of these lesions have reported a low risk of local recurrence [8,9]. There have, however, been few studies directly comparing curettage with en bloc resection of benign aggressive bone lesions. Guo and colleagues [4] compared intralesional curettage with resection of acetabular giant cell tumor and reported similar functional outcome on the MSTS score [2], higher complications with resection, and decreased local recurrence with resection. The use of navigation has been shown to be associated with more accurate osteotomies, thereby preserving both the margin around the tumor and bone stock [5-7]. Where Do We Need To Go? Although some studies [5,6] have focused on malignant lesions where obtaining a negative margin is essential, the paradigm of treatment is different with benign aggressive lesions because local recurrence is not correlated with worse overall survival as it is with malignant lesions [3,10]. Studies [8,9] have shown that treatment with curettage and reconstruction is effective in providing local control of the tumor amongst the more-common benign, aggressive lesions. That being so, there should be consideration of the balance in exposing these generally young patients whose tumors are unlikely to be life threatening to the lifelong downsides associated with major reconstructions that follow en-bloc resections. But if we are to answer this question more definitively, a patient-reported outcomes tool should be utilized rather than a clinician-completed one such as the MSTS score in order to incorporate the patient perspective into the analysis. The Toronto Extremity Salvage Score (TESS) [1] might work well for this, and we might supplement it with an outcomes tool that focuses on overall health-related quality of life (HRQL). Should en bloc resection be found to be the preferred treatment method, then it becomes necessary to determine whether such resection should be performed with or without navigation. In part, this determination may be site specific as the technical complexities of a pelvic resection is different compared to appendicular sites. How Do We Get There? Answering these important questions is challenging because of the relative rarity of these tumors, making adequate sample sizes difficult to come by; researchers will need to perform collaborative, multicenter studies to overcome this. A comparative study between curettage and resection, stratified on the anatomic location, will provide the necessary data to determine the better treatment strategy. These studies should incorporate oncologic outcomes, specifically local control, complications, and a measure of patient-reported outcome, as well as function and HRQL. Researchers should consider cost-effectiveness studies comparing en-bloc resection to intralesional treatments. Such studies should examine: (1) The costs of reconstruction with large-segment allografts, (2) prosthetic reconstructions, (3) future revisions of these constructs, which are complications unique to en-bloc resection, and (4) treating recurrences, which are likely to be more common following intralesional treatment. Finally, if en-bloc resection is shown to be superior (either in terms of efficacy or cost-effectiveness), then we should investigate whether such resections benefit from computer navigation. These studies should focus on one anatomic site (such as the pelvis), or, if the sample sizes are large enough, the analyses could be stratified by anatomic site. Because navigation is expensive, and it adds surgical time, cost effectiveness of navigation will need to be assessed.

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)0,002
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,071

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0210,004

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 Opus0,079
Tête enseignante GPT0,423
Écart entre enseignants0,344 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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