Abstract P1-01-18: Survey of the Current Practice of Sentinel Lymph Node Biopsy in Latin America
Notice bibliographique
Résumé
Abstract Background: Despite the fact that breast cancer cases in Latin America (LA) are frequently diagnosed in advanced stages, a significant number of cases are detected early on. Sentinel lymph node biopsy (SLNB) is now the standard of care for nodal staging in early breast cancer and it should be offered to patients even in countries with limited resources. Currently there is limited information regarding practice of SLNB is in LA. The purpose of this study is to explore how SLNB is offered in LA to breast cancer patients when properly indicated, and to investigate the possible obstacles to its implementation. This information would allow us to assess the need for education strategies to expand its use and maintain quality. Methods: An original electronic survey questionnaire was developed to assess self-reported practice in SLNB of all surgeons involved in the management of breast cancer in LA. Moderate to high volume practice was defined as 4 or more breast cancer cases per month or when it comprised more than 25% of the practice. Questionnaires were sent out by e-mail to surgeon members of each of the national surgical associations and/or national mastology societies. For this initial report we selected the following countries: Mexico, Guatemala, Nicaragua, Colombia, Venezuela, Peru, and Uruguay. We present a descriptive analysis of these responses. Results: 330 surgeons responded, of whom 218 (66.1%) were general surgeons and 112 (33.9%) had a subspecialty in breast surgery or surgical oncology. Only 31.8% (105/330) of surgeons reporting to treat breast cancer had a moderate to high caseload. In cities with less than 500,000 inhabitants, breast cancer cases were mainly treated by low volume general surgeons. In larger cities, there were a greater number of specialized surgeons; still a significant number of low volume surgeons are taking care of breast cancer cases. Out of 105 surgeons with moderate to high volume, 93 (88.6%) routinely perform SLNB; of whom more than half (53.8%) perform 5 or more SLNB per month. Nonetheless, 45.2% stated that less than 25% of their caseload was eligible to undergo SLNB. The majority (69.6%) reported using the combined technique (dye and radiotracer). Most (67.9%) learned SLNB technique with a mentor or during fellowship training. When asked about limiting factors to the implementation: 61% highlighted the lack of resources such as gamma probe, nuclear medicine facilities and increased costs as a restrictive factor, and 32.4% pointed out the lack of training opportunities as their main limitation. Just 32 surgeons were involved in SLNB teaching in post-graduate medical training but only 12 of them referred to teach an appropriate number of cases to residents. Conclusion: A significant number of the surgeons treating breast cancer are general surgeons with a low volume of cases but it amounts to a significant number of patients. SLNB is currently been done by surgeons with a moderate to high caseload and/or a sub-specialty. Barriers to implementation appear to be related to scarce resources, lack of training opportunities and low volume of eligible cases. There seems to be a need for increase use of SLNB in LA. Novel strategies to train surgeons and guarantee the quality of their practice are warranted. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P1-01-18.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».