MétaCan
Menu
Back to cohort

Innovative approach to delivery of TB Medicines

2022· article· en· W4313390731 on OpenAlexaboutno aff
Yatin Dholakia

Bibliographic record

VenueLung India · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicPharmacyAgency (philosophy)Work (physics)Public healthEconomic growthCoronavirus disease 2019 (COVID-19)Family medicineNursingInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Sixty-seventh World Health Assembly approved the End TB Strategy[1] to achieve United Nations SDG 3 targets. The strategy proposes three pillars: integrated patient-centred care and prevention; bold policies, supportive systems and intensified research and innovation.[2] India’s National Strategic Plan 2020–2025[3] envisages ending TB by 2025 ahead of the global strategy. National TB Elimination Program has pressed in all efforts to bring this to fruition by enlisting multisectorial support. A key imperative to eliminate TB is reducing the pool of infectious cases. Fundamental to this is the early identification of cases and treating them promptly and completely. TB treatment is arduous and rigorous adherence is essential to achieve a cure. This presupposes equitable access to medicines. Challenges to access and adherence to TB treatment are: (a) inconvenient timings of the public health institutes may overlap work hours or involve long travel distances to collect medicines which often lead to drop out or interruptions. (b) Migration, frequent travel either on a job or seasonal for activities related to agriculture, festivals, family functions, or due to unforeseen circumstances. Natural calamities and the ongoing Covid19 pandemic are major reasons for mass migrations. Migration affects the continuity of treatment and may lead to the development of drug resistance and increased transmission of TB. (c) The large private sector in India is being engaged through a phased implementation of Public Private Support Agency program where medicines are provided through approved pharmacies, which are few and far and access to these are also not universal. These challenges are indeed real but surmountable. It is of great importance that TB patients can avail treatment once diagnosed anywhere and at any time. We need to learn to improve access to medicines from other sectors. Banks manage Any Time Money (ATM) effectively across the length and breadth of the country in compliance with the national statutes and within the available resources of the client. Similarly, vending machines are popular for a variety of products. These require little space, reduce labour cost, provide excellent information connectivity and monitoring remotely. India’s TB program is way ahead of other countries in care delivery in more ways than one. The digital information ecosystem with its real-time data management through the NIKSHAY portal is commendable. Through this portal, cases can be assessed for diagnosis, treatment and direct benefit transfer for Nikshay Poshan Yojna, notification by private providers is being undertaken and the new innovative nutritional support through the Nikshay Mitra will also be synchronised. This portal has the potential to be the backbone of any future development in tuberculosis care and support. India is a leader in information technology (IT). Artificial intelligence (AI)-based solutions can address the issue of scarce personnel, laboratory facilities and also help overcome barriers to access.[4] The use of AI in radiological diagnosis has been a boon for rural areas where experts are few and far. There is already a lot of interest in public health interventions from the industry.[5] This can be tapped through initiatives under the Atma Nirbhar Bharat schemes and support to start-ups, specially to move towards digital India initiatives. THE CONCEPT: ANY WHERE MEDICINES (AWM) Imbibing from the banking institutions and combining with the expertise in both the TB program and IT industry, TB medicines can be made accessible to the remotest of villages in the country through medicine vending machines which are designed and tailored to incorporate all components of the program, thus achieving the concept of AWM. Briefly, diagnosed individuals can be given a QR-coded prescription which can be read by the machine. This will identify the drugs and the doses to be dispensed. Being internet enabled, the event can be directly recorded on Nikshay and allow the program to keep track of patients ensuring that they do not drop out of treatment. Follow-up symptom assessment and adverse drug reaction management can be done 24 × 7 via telemedicine through specific system programming. The concept of medicine vending machines is not new. Prescription drug dispensing machines have been in use in Canada for over 5 years.[6] In South Africa, ATM pharmacies provide access to HIV medicines.[7] Currently, the medicine vending market is growing at 7% annually.[8] Various players are engaged in developing advanced solutions by integrating modern technologies. The machines can be installed in any covered location like gram panchayat offices, post offices, bus depots, banks and other public places. The key considerations are the availability of security and support partnership, built in UPS/power supply, internet connectivity, climate control to preserve drug potency, among a few. This could be piloted in some key towns and scaled up based on results. This innovative approach to improve access and delivery of TB medicines to the last case in remote areas will bring us closer to our goal of ending TB. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.330
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLung IndiaSame topicTuberculosis Research and EpidemiologyFrench-language works237,207