Exploring the use of decision support tools to evaluate cancer predisposition syndromes in pediatrics
Notice bibliographique
Résumé
Background: Cancer predispositions syndromes (CPSs) are genetic conditions that increase the likelihood of developing cancer throughout a patient’s lifetime. For pediatric cancer patients, CPSs are particularly relevant, as this population is less likely to develop malignancies from environmental exposures or other cancer-associated lifestyle factors. In fact, recent advances in the field of cancer genetics have elucidated the importance of recognizing the multitude of CPSs that may impact treatment plans, cancer surveillance and/or preventative measures for pediatric patients and their families. As a result, the last 20 years have seen a rise in decision-support tools (DSTs) that aim to guide health care practitioners in their evaluations of underlying CPSs. Currently, the scope of DSTs used to evaluate pediatric CPSs has yet to be described and their clinical application across Canadian institutions is not well understood. Objectives: The primary goal of this thesis is to identify, describe and categorize the features of DSTs developed for the pediatric oncology population. The second goal is to establish how these tools are being adopted in clinical settings, by assessing their utility to pediatric hematologist- oncologists (PHOs) across Canadian tertiary-care hospitals. Methods: An initial scoping review was performed to identify the pediatric-adapted DSTs that utilize the patient’s clinical features to determine whether they are likely to have an underlying CPS. Using the Joanna Briggs Institute scoping review methodology, a systematic search strategy was developed and customized for MEDLINE and EMBASE databases. Subsequently, the tools identified in the scoping review informed a survey electronically distributed to PHOs across the 16 largest pediatric oncology departments in Canada. Their awareness and attitude towards DSTs were solicited on an anonymous basis. Results: Fourteen DSTs were identified, of which (8/14) (57%) have been internally or externally validated for clinical use. Half of the DSTs were specific to one CPS (7/14); the majority were published in a paper-based format (11/14); developed to input the patient’s tumour type (14/14), family history of cancer (12/14), non-malignant physical findings (8/14); and developed to output their recommendation in a dichotomous form (10/14).With the online survey, a total of 36 responses from PHOs were recorded: 18/36 (50%) of the respondents had previously used a DST, while 15/36 (41.7%) had not, and 3 were uncertain. Users of DSTs did not solely rely on the tool’s recommendation but used it as part of their decision-making process. Non-DST users were often unaware of the existence of these tools or how to gain access to them. Both DST users and non-users stated that a tool’s ease-of-use, its accessibility, and its promotion by their academic institution constitute the most important features for a tool’s adoption into their clinical practice.Conclusion: Fourteen pediatric CPS DSTs were identified through a scoping review; these were developed with a wide range of input/output parameters, formats, and types of CPSs and malignancies being evaluated. Despite the need for additional resources, the use of DSTs in clinical settings is not prominent, as half of the surveyed physicians have not previously used a DST and most tools remain unknown to them. With further development of DSTs’ ease-of-use, accessibility, and evidence of their clinical benefit, adoption of DSTs in clinical practices across the country may become more systematic and lead to the increased recognition of CPSs in pediatric patients
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 | 0,050 | 0,207 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,029 | 0,026 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».