MétaCan
Menu
Retour à la cohorte
Enregistrement W4392870398 · doi:10.4103/crst.crst_190_22

No more relay of the delay: Passing the baton to the digital technologies

2022· article· en· W4392870398 sur OpenAlexaboutno aff
Harsh Priya, M.S.S. Bharathi, Pallavi Shukla, Deepika Mishra

Notice bibliographique

RevueCancer Research Statistics and Treatment · 2022
Typearticle
Langueen
DomaineComputer Science
ThématiqueCooperative Communication and Network Coding
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRelayTelecommunicationsComputer sciencePhysicsPower (physics)

Résumé

récupéré en direct d'OpenAlex

We read with great interest the article by Singla et al.[1] titled, “Impact of demographic factors on delayed presentation of oral cancers – A questionnaire-based cross-sectional study from a rural cancer center,” in the previous issue of the journal. The glaring finding that was an eye-opener for us was that the lag from the onset of symptoms to the medical consultation was most often reported as 3 months, and that to the cancer diagnosis was 5.5 months. The common causes of this delay could be categorized as patient-related or health professional-related. Social media and tele dentistry have the superpowers to tackle both these barriers in the early detection of oral premalignant lesions and oral cancer. The Government of India has launched the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS),[2] under which oral cancer has garnered a lot of attention. There were screenings and oral health checkups conducted for targeted and general populations.[3] Along with these programs, the National Tobacco Control Program (NTCP),[4] and National Oral Health Program (NOHP),[5] also started with tobacco cessation counseling and promotion of oral health. The need of the hour is to follow-up on these screened individuals through mobile application tracking technologies. An interactive mobile application would enable the patient to fulfill his/her responsibility to firstly upload the usage pattern of his/her tobacco and other risk factors, and subsequently to report distantly any change in the identified lesions in their oral cavity. Such an application would also allow the healthcare professional to encourage behavior modification and regular oral health consultation. This two-way health communication could initially occur in person, and once the patient has been registered in the mobile application, he/she could be followed up digitally. In case of any red flags, immediate communication and referrals to the tertiary center can be done thereby shortening the delay. The mobile application with features of interactive chats especially in local languages would empower the patients to clear their slightest doubts and thus, to nip them in the bud. The facility provided by the application for clicking and uploading the images of any change in the oral lesion by the patient would again make them feel connected to the health care professional, thereby removing the distance barrier. Digital technologies are impacting the health sector in a beneficial manner. The only challenge is the digital illiteracy[6] and denial of the individual’s health freedom. Reorienting the health education system with digital technologies would be the most appropriate strategy to follow the principles of the Ottawa Charter in health promotion.[7] There will never be enough tertiary care facilities, hence the primary prevention of oral premalignant lesions and oral cancers through mobile applications could be a game changer in lessening the delay of early detection and prompt treatment. Hence, the baton of oral cancer diagnosis needs to be passed on to the digital technologies, to together fight the battle against the disease. Financial support and sponsorship This manuscript has been developed in support of blending digital technology for early detection and prompt treatment of oral premalignant lesions and oral cancer Conflicts of interest There are no conflicts of interest.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,915
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
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 Opus0,089
Tête enseignante GPT0,383
Écart entre enseignants0,295 · 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
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueCancer Research Statistics and TreatmentMême sujetCooperative Communication and Network CodingTravaux en français237 207