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Expanding access to care through sustainable replicable model of care delivery.

2025· article· en· W4410795688 on OpenAlexaboutno aff
Dinesh Pendharkar, Chandramauli Tripathi

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNursing

Abstract

fetched live from OpenAlex

e13862 Background: Access to equitable care remains a major challenge. In most countries, access to specialized care is limited because of socioeconomic, geographical, and human resource constraints.To meet the challenge, multiple models are being tested worldwide: Buttaro, Australian, Canada, Montana, and District Cancer Care Model (Pendharkar model). The district model has evolved over more than ten years and has proven its sustainability, replicability, acceptability, and scalability. We offer a review of the 10 years of progress. Methods: In 2014, a cancer model was designed based on WHO framework of strengthening the health system. District hospitals in India are the peripheral pillars of the state-owned health system, where comprehensive care is feasible. Multiple regulations have been introduced to create district cancer units, empowering the delivery of cancer care from diagnosis to end-of-life. One physician was trained over a short period and offered a 24x7 hub and spoke mentorship in one state. In follow-up, other states of India independently reviewed the program and extended. Results: After launch in February 2014, over the last ten years, the program has been extended to eight states, nearly 200 districts, covering an area of 1200 million square kilometers and a population of 400 million. Five more states are committed to initiating the district cancer care model in 2025. More than 290 physicians and 550 nurses have been trained . The program offers consultation and chemotherapy services ,including free drugs,to more than 300,000 patients.The same units were trained to provide palliative care. The funding for the program is generated internally by the state and is available sustainably. The chemotherapy list has evolved annually and has gradually increased from 20 to 150 in a few states. It includes a complete basket of WHO essential oncology drug list. Conclusions: To date, this district cancer model based on the creation of an alternate oncology workforce using existing physical and human resources appears to be the most sustainable replicable model. Government-owned health systems, especially in LMIC, could be the single most important point of empowerment. Primary physicians can effectively offer patient-centric, consistent, and comprehensive services. Building and maintaining a decentralized and sustainable cancer care program is feasible. The district cancer care model can assist in improving access to care and resolving disparities. Data from the Rajasthan state district cancer care programme. Year Outpatient Visits Patient Number New In Patient Visits IV Chemo Oral Chemo Palliative Care 2018-19 48153 8069 20648 13070 2796 7709 2019-20 23113 3873 9911 6274 1342 3700 2020-21 37458 4781 16187 11751 2602 6040 2021-22 29937 4339 15883 11879 2681 5815 2022-23 33335 4887 16934 12316 3167 6204 2023-24(upto Oct. 23) 24144 2806 11756 10162 2381 4369 Total 196140 28755 91319

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0020.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0290.005

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 head0.100
GPT teacher head0.507
Teacher spread0.408 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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