MétaCan
Menu
Retour à la cohorte
Enregistrement W2495836156 · doi:10.1120/jacmp.v17i4.6510

Response to Re: Estimating and reducing dose received by cardiac devices for patients undergoing radiotherapy

2016· article· en· W2495836156 sur OpenAlexaff
Louis Archambault, Nicolas Varfalvy, Alexandra Bourgouin

Notice bibliographique

RevueJournal of Applied Clinical Medical Physics · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiation Dose and Imaging
Établissements canadiensCentre hospitalier universitaire de Québec
Organismes subventionnairesnon disponible
Mots-clésElectromagnetic shieldingDetectorMedical physicsDiodeRange (aeronautics)Computer scienceEquivalent dosePhysicsNuclear medicineMedicineDosimetryMaterials scienceOptoelectronicsTelecommunications

Résumé

récupéré en direct d'OpenAlex

To the Editor: We have read the comments1 made regarding our recent manuscript(2) and we welcome the chance to address some of the issues raised in that letter to the editor. First and foremost, we would like to clarify that our manuscript does not intend to recommend the widespread use of lead shielding as protection for cardiac implanted electronic devices (CIEDs). Our goal was twofold. First we wanted to present a simple model to estimate out-of-field doses and use this model to assess doses that can be delivered to CIEDs. Second we wanted to quantify possible dose reductions resulting from the use of a lead shielding. While dose reductions were indeed observed, they may not always be significant. where d is the radial out-of-field distance. Thus Eq. (2) is the same as the one proposed by Mihailidis.1 As pointed out,1 most measurements involved in our work were taken in conditions that are unusual for clinical measurements: out-of-field and low doses at shallow depths. Such measurements can be challenging for most detectors and require careful considerations. The excellent linearity and good energy independence of a plastic scintillation detector (PSD)(3–6)are actually the main reasons that motivated the use of a PSD over more common detector types, such as silicon diodes that overrespond to low photon energies due to the higher interaction probability per unit mass of Silicon compared to water. PSDs like the W1 have been shown to be water equivalent over a large range of energy, including radiological energies,7 which makes it ideal for out-of-field measurements. Furthermore, side-by-side comparison between the Exradin W1 and ion chambers (an IBA CC04 and a Exradin A11 parallel plate chamber) for out-of-field measurements were also performed, and a manuscript on that topic is currently in press.8 Measurements at a distance of 3 cm outside a field made with both the W1 and the CC04 agreed to within 5% for depths greater than 1.5 cm for 6 MV and 23 MV beams. At shallower depths, the dose gradient varied more rapidly and discrepancies up to 10% were observed. In these cases the poorer spatial resolution of the ion chamber could explain part of the observed differences. Comparison between a parallel plate ion chamber and the W1 agreed to within 0.2 and 0.1 cGy for doses of 0.5 cGy for photon and electron beams, respectively. These discrepancies were within the estimated statistical uncertainties. Having been able to measure small surface doses out of the primary radiation field with a detector calibrated using the manufacturer's recommended procedure is, in our opinion, a testimony of the great versatility of PSD detectors. When comparing doses between 6 MV and 23 MV it is important to recall that our measurements were taken at a depth of 1.5 cm (the average depth of CIED at our institution), which is shallower than other published data. For example, Fig. 18 of TG-369 presents a comparison between energies measured at 10 cm depth. It is thus difficult to compare measurements at such different depths. Nevertheless, when looking at posterior-anterior (PA) beams that are necessarily made at a much greater depths, our measurements are indeed relatively independent of beam energy. Our observations at shallow depths appear to be in line with other surface dose measurements close to the field edges.10, 11 It is important to stress again that the point of our manuscript is not to propose or recommend the use of lead shielding as a perfect solution to protect CIEDs, but instead to show that such shielding is a simple and inexpensive method of reducing doses to CIED. We agree that results can greatly vary depending on the linac type and the geometry of its head. Linacs from other manufacturers will likely show a different behavior. Nevertheless, the simple model we propose could be fitted to any data and help estimate dose to CIED in regions where treatment planning systems are well known to offer suboptimal dose calculation accuracy. In that regard, Fig. 22 should be seen as a demonstration that our dose estimation model can work in different situations, rather than a recommendation to use lead shielding. In our clinic, lead covered in thermoplastic was used because it is easy to manipulate and position on the patient, but we agree that other materials could be used to the same effect. There is mounting evidences that the presence of neutrons is strongly correlated with CIED events during radiation therapy,12 but CIED events are also observed in patients treated with fields that do not produce neutrons. Therefore, adding some shielding that can reduce the dose received by CIEDs is well aligned with the ALARA (i.e., as low as reasonably achievable) principle of radiation protection. Nevertheless, the pertinence of using a shielding when treating CIED should be evaluated carefully by any clinic. This work is licensed under a Creative Commons Attribution 3.0 Unported License.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,947
Score d'incertitude au seuil0,794

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,026
Tête enseignante GPT0,368
Écart entre enseignants0,342 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
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
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Applied Clinical Medical PhysicsMême sujetRadiation Dose and ImagingTravaux en français237 207