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Enregistrement W2155490445 · doi:10.1258/jrsm.2011.110311

Misplaced criticism of breast screening research

2012· letter· en· W2155490445 sur OpenAlexaboutno aff
Stephen W. Duffy

Notice bibliographique

RevueJournal of the Royal Society of Medicine · 2012
Typeletter
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOverdiagnosisBreast cancerMedicineIncidence (geometry)DemographyQuarter (Canadian coin)CancerGynecologyInternal medicineHistoryMathematics

Résumé

récupéré en direct d'OpenAlex

Dear Sir, The paper by Gotzsche and Jorgensen1 contains a number of inaccuracies and omissions in its criticism of my work and that of my colleagues.2 Firstly, Gotzsche and Jorgensen take exception to the quoted 50% improvement in breast cancer survival in screen-detected cancers. They omit to mention that the approximate 50% improvement is after correction for lead time and length bias – before correction, the figure was a 70% improvement.3,4 Secondly, they claim that the 28% reduction in breast cancer mortality in England in the screened ages compared to other ages did not occur. They are mistaken. Table 1 shows breast cancer mortality by epoch in ages 50–69 and in all other age groups. While mortality rose by 2% in the latest period compared to the earliest for all other age groups, it fell by 27% in the age group 50–69. The relative risk of breast cancer mortality is therefore: Table 1 Breast cancer mortality in England by age group and epoch That, is a 28% reduction compared to other age groups. In the published analysis, the estimate was age-adjusted, but I give the crude analysis here so that readers can see where the estimate comes from. Gotzsche and Jorgensen's criticisms are particularly error-prone on the subject of over-diagnosis. It might be illuminating to contrast our approach2 with that of Jorgensen and Gotzsche.5 Both teams attempted to estimate overdiagnosis by calculation of expected incidence of breast cancer in the screening epoch based on trends observed in the pre-screening epoch. However, the methods differed at each stage, as follows: Data Sources: Duffy et al. used data on numbers of cases and populations at risk in England from Cancer Registry data.2 Jorgensen and Gotzsche estimated rates for England and Wales from a published graph. Changes in Incidence Independent of Screening: Duffy et al. took full account of these changes by correcting for the 7% increase above expected values at ages below the target age group for screening.2 Jorgensen and Gotzsche failed to do so.5 Method of analysis: Duffy et al. used poisson regression, as is the correct procedure for rate data.2,6 Jorgensen and Gotzsche used linear regression, which is incorrect.5 Data selection: Duffy et al. used all pre-screening and screening epoch data available.2 Jorgensen and Gotzsche excluded the three years of highest incidence in the pre-screening period, and only included the year of highest incidence in their screening epoch data, 1999.5 This inflated their estimate of overdiagnosis. Adjustment for lead time: Duffy et al. subtracted the deficit in incidence above the screening age from the excess observed in screening ages.2 Jorgensen and Gotzsche failed to do so.5 To be fair, they claimed not to observe a deficit. This is partly because in 1999 too few women above the screening age range had been screened in the past, but also because of their failure to fully adjust for changes in incidence independent of screening, as noted in point 2 above. Ductal Carcinoma in situ (DCIS): In the absence of data, Duffy et al. restricted estimation to invasive disease, although in the same paper, they estimated overdiagnosis including DCIS in a randomized trial.2 Like Duffy et al, Jorgensen and Gotzsche had no data on DCIS in the UK, so they assumed a result which was not observed.5 From the above, it can be seen that our modest estimate of overdiagnosis has more reliability than the implausibly high estimate of Jorgensen and Gotzsche. Gotzsche and Jorgensen make a number of further errors in defence of their estimate, including: failure to acknowledge that in the 1990's in the age range for screening, a full paper (not an abstract as stated by Gotzsche and Jorgensen),7 has shown that around 40% of tumours were screen-detected; citation of figures from 2006 to justify their estimate for 1999; and misinterpretation of those figures from 2006, as pointed out previously.8 More importantly, one should not lose sight of the benefit of the NHS Breast Screening Programme and the fact that the only randomized trial with more than 25 years of follow-up, shows that the quoted benefit of one breast cancer death prevented for every 400 women screened is accurate and may even underestimate the benefit.9

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)0,184
score de la tête « metaresearch » (Gemma)0,550
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,184
Score d'incertitude au seuil0,974

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1840,550
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0050,003
Bibliométrie0,0110,011
Études des sciences et des technologies0,0060,046
Communication savante0,0160,015
Science ouverte0,0130,010
Intégrité de la recherche0,0290,058
Charge utile insuffisante (le modèle a refusé de juger)0,0080,010

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 Opus0,154
Tête enseignante GPT0,405
Écart entre enseignants0,252 · 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
Publié2012
Routes d'admission1
Résumé présentoui

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