Establishing childhood cancer survivorship clinics in India: A consensus statement
Bibliographic record
Abstract
Provide a consensus statement describing best practices and evidence regarding the setting up Childhood Cancer Survivor Clinics in India. MethodsKey topics regarding childhood cancer survivorship clinics were identified during a workshop conducted during the annual Pediatric Hematology Oncology conference (PHOCON) in Delhi in November 2022. Workshop participants included oncologists, hematologists, medical social workers, representatives of non-government organizations, and childhood cancer survivors. Consensus was generated by combining expert opinion and a review of the literature. Several components regarding survivorship clinics, including the setting up of survivor clinics separate from the oncology clinic, the leading role of the treating oncologist in survivor clinics, the composition of survivor clinics and the patient pathways in the clinic, the frequency of follow-up for different survivors based on risk stratification, plans in adult survivors and the role of allied specialties like cardiologists, neurologists etc. were discussed. Care of childhood cancer survivors is complex and requires a multidisciplinary approach centred around patients and their caregivers. Addressing post‐treatment concerns is critical to our patient's quality of life as survival improves. There continues to be a need to define effective and efficient programs that can coordinate this multidisciplinary effort toward survivorship.
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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 | 0.097 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".