DNA Ploidy Cytometry Testing for Cervical Cancer Screening in China – Letter
Notice bibliographique
Résumé
We read the article of Tong et al. (1) with great interest. However, on closer analysis, we found several points that need clarification.Although the authors call this a “randomized controlled trial,” both tests were done on almost all of the study subjects and the data presented in Table 2 is pooled from both arms of the trial. Nowhere, including in the supplementary data, are the results for each separate arm of the trial reported. We believe that it should be possible to check the values for “Crude estimates” in Table 3 for cancer cases from the data presented, but we have not been able to do this. In fact, it is unclear to us how many cancer cases were found in this study.Table 2 reports that DNA gave positive results for ∼6,000 of 21,500 cases, and yet the “crude” specificity reported in Table 3 is <60% rather than >70% as implied by these numbers. This enormous false-positive rate is not commented on, and yet, apparently, the positive predictive value of DNA is higher than that for cytometry, which has half as many false positives. The true positive rate is unclear, but at most is only 100—much less than 6,000.In the abstract, it states that: “The sensitivity of both tests used together was 100%, and the specificity was 91.8%.” We could not find this calculation anywhere in the article itself and think it unusual to report a result only in the abstract. It can be shown that there are only two ways by which test results such as this can be combined: as a logical “or” of positive tests results (that is, the combined test result is positive if either test is positive) or as a logical “and” of test results (that is, the combined test result is positive only if both tests are positive). It can be shown that the “or” will increase sensitivity and decrease specificity whereas the “and” will decrease sensitivity and increase specificity. Yet these authors claim to improve both sensitivity and specificity simultaneously.The “100%” sensitivity obtained by combining the tests is by construction of the experimental design. Pap screening sTudies rarely have an independent and reliable reference diagnosis (for example, biopsy tests on all subjects), but only compare the results of two tests. There is no way to know how many positive cases actually exist in the study population. Such studies only measure a kind of relative sensitivity and specificity that compares one test with the other—absolute sensitivity and specificity is not determined. Because only DNA and cytology were used to discover cancer cases, the logical or of their positive results must be 100% sensitive, by experimental design.Finally, given that the results reported by these authors seem to be pooled from both arms of the trial, we fail to understand what makes this a randomized controlled trial.We respectfully ask for careful clarification of these points.See the Response, p. 3517.D. Garner: consultant, Motic Medical Diagnostic Systems; M. Guilland: consultant/advisory board, Novacyt.
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 | 0,006 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,019 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
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 ».