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Enregistrement W3161660482 · doi:10.1093/jnci/djab094

Response to Zhang and Yang

2021· letter· id· W3161660482 sur OpenAlexaff
Torsten O. Nielsen, Samuel Leung, Lisa M. McShane, Mitch Dowsett, Daniel F. Hayes

Notice bibliographique

RevueJNCI Journal of the National Cancer Institute · 2021
Typeletter
Langueid
DomaineMedicine
ThématiqueClinical Laboratory Practices and Quality Control
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésZhàngChemistryPolitical scienceChinaLaw

Résumé

récupéré en direct d'OpenAlex

We thank Drs Zhang and Yang for their comments regarding Ki67 immunohistochemical assessment. We would like to emphasize that we are not claiming there is stronger concordance in absolute Ki67 index scores when they are below 5% or above 30%, but rather that those values are sufficiently different from prognostic decision cutpoints that, despite the residual analytical variability that remains after standardization, results are sufficient for the purpose of deciding on the need for adjuvant chemotherapy in anatomically favorable ER-positiveand HER2-negative patients. To illustrate this, consider the scores reported in Figure 1, republished from Leung et al. (1). For all cases with at least 1 laboratory reporting a score of 5% or less, the median values of all laboratories in these cases range from 4% to 8%, which is below typical clinical thresholds (10%-15%). Similarly, in this same data set, the cases with at least 1 laboratory reporting a score of 30% or greater, the median values of all laboratories in these cases ranges from 19% to 88%. These conclusions not only are based on the data from our own group but also are supported by several other published studies (2,3). Immunohistochemical testing for Ki67 remains a very practical and widely available test applicable in many jurisdictions, some of which do not have ready access to more complex testing modalities. Despite its limitations in quantitative precision, when technical quality assurance is in place and scoring is standardized, this test retains real value for patient care in specific clinical contexts. Heat map of Ki67 scores. Rows represent cases and columns represent laboratories. The color green indicates that the score is less than 10%, yellow 10% to 20%, and red greater than 20%. Cases are ordered by the median scores (across laboratories), which are shown in parentheses beside the specimen number. Laboratories are ordered (within each group) by the median scores (across cases). The 3 colon-separated numbers to the right of the table represent the number of laboratories giving scores falling into different ranges: less than 10% (left-most), 10% to 20% (middle), and greater than 20% (right-most). For example, 15:6:1 indicates that 15 laboratories gave a score of less than 10%, 6 laboratories from 10% to 20%, and 1 laboratory greater than 20%. Republished from Leung et al. (1). Please refer to the original article (1) for the color version of this figure. Heat map of Ki67 scores. Rows represent cases and columns represent laboratories. The color green indicates that the score is less than 10%, yellow 10% to 20%, and red greater than 20%. Cases are ordered by the median scores (across laboratories), which are shown in parentheses beside the specimen number. Laboratories are ordered (within each group) by the median scores (across cases). The 3 colon-separated numbers to the right of the table represent the number of laboratories giving scores falling into different ranges: less than 10% (left-most), 10% to 20% (middle), and greater than 20% (right-most). For example, 15:6:1 indicates that 15 laboratories gave a score of less than 10%, 6 laboratories from 10% to 20%, and 1 laboratory greater than 20%. Republished from Leung et al. (1). Please refer to the original article (1) for the color version of this figure. None. Role of the funder: Not applicable. Disclosures: Torsten O. Nielsen (T.O.N.) received royalty from NanoString Technologies. T.O.N. has intellectual property rights and hold patent with Bioclassifier LLC. Mitch Dowsett received lecture fees from Nanostring and Myriad; participated in advisory/consultancy role with Radius, Lilly, AbbVie, H3 Biomedicine and Zentalis. His institution received grants from Pfizer and Lilly on studies that includes Ki67 analysis. Daniel F. Hayes (D.F.H.) reports research support from Menarini Silicon BioSystems (MSB). The University of Michigan (UM) holds patent US 8,790,878 B2 for which D.F.H. is designated as inventor, and that is licensed to MSB with annual royalties paid to UM and D.F.H. Outside the submitted work D.F.H. holds stock options from OncImmune LLC, InBiomotion, and serves on advisory boards for Cepheid, Freenome, CellWorks, Agendia, Salutogenic, EPIC Sciences and L-Nutra and UM receives research funding on his behalf from Merrimack, Eli Lilly, Puma Biotechnology, Pfizer, AstraZeneca. The remaining authors have no conflicts of interest to disclose. Author contributions: Torsten O. Nielsen contributed to conceptualization and writing—original draft, review and editing. Samuel C.Y. Leung contributed to conceptualization and writing—original draft, review and editing. Lisa M. McShane contributed to conceptualization and writing—original draft, review and editing. Mitch Dowsett contributed to conceptualization and writing—original draft review and editing. Daniel F. Hayes contributed to conceptualization and writing—original draft, review and editing. Not applicable.

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)0,005
score de la tête « metaresearch » (Gemma)0,017
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,017
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,003
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 Opus0,143
Tête enseignante GPT0,434
Écart entre enseignants0,291 · 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.

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

Citations5
Publié2021
Routes d'admission1
Résumé présentoui

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