No more relay of the delay: Passing the baton to the digital technologies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.034 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".