Patient tracking during treatment of children with cancer in India – An exploratory study
Bibliographic record
Abstract
BACKGROUND: Abandonment of treatment, a major cause of treatment failure in low- and middle-income countries like India, is particularly high during the diagnostic and initial phase of treatment. Tracking of patients during this risk period may reduce treatment abandonment rates and increase quality of care. AIM: The primary aim was to pilot the use and check the acceptability of a tool for tracking children with cancer in New Delhi during the initial part of their treatment. Secondary aim was to estimate abandonment rates among these patients. METHODS: This prospective study was carried out in two centers of North India in New Delhi and enrolled children less than 18 years diagnosed with cancer at these centers and who had registered with Cankids for social support. Parent support group (PSG) workers maintained contact with the child's family at least once a week for the first 12 weeks. Details of each contact and subsequent action were recorded in a customized book (called "You are not alone" or YANA Book). Descriptive analysis of these contacts was done in Microsoft Excel and presented in frequencies and percentages. The five-point Likert scale was used to check the acceptability of the tool among the PSG workers. RESULTS: Seven PSG workers enrolled and tracked 81 patients (73% male with a median age of 6 years). During the 12-week study period, 986 contacts were attempted and three (3.7%) patients had abandoned their treatment. All PSG workers strongly agreed that the YANA book was simple to understand and use, decreased their workload, and helped provide better assistance to patients. CONCLUSION: The tool for patient tracking was well accepted by the PSG workers and considered easy to use. We now plan to implement our model as a routine service at all the partnering hospitals in India.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".