Genetic counselling in India: The state of affairs
Bibliographic record
Abstract
The literature review by Ulhaq et al. has very rightly touched upon a topic of great value in today’s era of precision oncology.[1] Next-generation sequencing (NGS) panels test for mutations in a large number of genes, increasing the yield of pathogenic, and likely pathogenic gene variants.[2] This has increased the detection rate of mutations responsible for hereditary cancer syndromes.[2,3] The number of genetic counselors in India does not parallel this demand, and there is a huge unmet need. Abacan et al. in their 2019 analysis titled, “The Global State of the Genetic Counseling Profession” have highlighted this unmet need.[4] The genetic counseling program in India was started in 2007, and there are only approximately 76 genetic counselors to serve a population of 1.3 billion people. On the contrary, the genetic counseling program in the United States was started in 1969, and the number of genetic counselors is around 4000 for a population of 0.3 billion people.[4] The cancer predisposition syndrome (CPS) screening tools, especially Jongman’s criteria and its modified version (Jongman’s Modified Criteria [JMC]) deserve a mention.[5] Jongman’s criteria is the most widely used screening tool for patients suspected to have a CPS.[5] While the Childhood Cancer Screening Checklist (CSCC) and the McGill Interactive Pediatric Oncogenetic Guidelines (MIPOGG) screening tools focus only on the morphological evaluation of the patients, Jongman’s criteria and JMC focus on the family history and type of malignancy as well. Jongman’s criteria and JMC are the only screening tools that refer patients to a genetic counselor when they have suffered excessive cancer treatment-related toxicities or have tumors with genetic defects suggestive of a CPS. JMC assesses a wider list of CPS-associated tumors, and their questionnaire has been validated by Schwermer et al. in the German case-control study on newly diagnosed pediatric cancer patients.[6] The prevalence of CPS diagnosed with the help of the questionnaire was 9.4% as compared to the historical control group of 5.3%. Lastly, the psychosocial impact and patient perspectives are important topics related to genetic testing. A systematic review of 47 studies between the years 2000 and 2016 (20 of which dealt with cancer) was published by Oliveri et al. in 2018.[7] The team focused on assessing the psychological aspects (predominantly anxiety and depression) of genetic testing on patients and their relatives. The data revealed that genetic testing did not significantly increase patients’ distress and anxiety or hamper their quality of life. The genetic information was perceived by the patients as important preventive data aiding the ongoing treatment.[7] Nevertheless, genetic counselling needs strong psychological support which should take into account the patients’ basic literacy, understanding of the risks, and beliefs about the disease process. 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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".