Correction: On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB)
Notice bibliographique
Résumé
A correction refers to a change to their article that the author wishes to publish after publication. The publication of this article is subject to Frontiers' editorial approval.Instructions:• Please read through all the templates before choosing • Pick the most relevant text template(s) from the following page and delete all others.• Edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below. • Please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary • Submit to Frontiers following the instructions on this page.When the original text contained incorrect information, to preserve the scientific record, please include that text when editing the below templates. For example:There was a mistake in the Funding statement, an incorrect number was used.The correct number is "2015C03Bd051.". The publisher apologizes for this mistake.The original version of this article has been updated. We have incorrectly opined about the performance of a private company's third party software (Segmed Inc). The performance related to Canadian zip codes as well as other types of data was due to a user error and improper configuration of the tool by the authors. We have made the following edits:[Methods, Anonymization of DICOM files and de-identification of clinical reports, second paragraph]. This paragraph should be replaced with:"For clinical report texts, we adopted and compounded the effect of the following three independent approaches as a conservative de-identification strategy: (1) Segmed Inc.'s Python-based web server was used to remove PII/PHI from clinical reports, (2) RegEx rules were used to remove Canadian formatted addresses and postal codes in Python, and (3) resulting texts were fed to a Microsoft Copilot agent that was instructed to list suspected people names, addresses, street names, 5-8 digit numbers, business names, clinic names and occupations. The agent was further prompted to ignore medical terms. The resulting terms were then manually screened for relevance, the terms were searched for in the text and then replaced with "[Anon]"."[Discussion, Data Ingestion, first paragraph]: This paragraph should be replaced with:"QA revealed our unstructured text data to be properly de-identified, highlighting the effectiveness of our multiple layers of de-identification approaches. Structured DICOM data from hospital sources proved straightforward to robustly de-identify using our strategy. Also, during our QA process, various non-structured DICOM tags, such as series description, used during splitting process were identified and addressed accordingly to preserve integrity of workflow." Accordingly, please remove reference 27.for a reason not seen here, please contact the journal's editorial office.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».