The current status of pediatric radiology in India: A conference-based survey
Bibliographic record
Abstract
INTRODUCTION: Like most other developing countries, India has a large proportion of children among its population. However, the facilities for adequate treatment of this large population is inadequate. The development of pediatric radiology as a subspecialty is still at an infant stage in India. The goal of our study was to assess the awareness about the current status of pediatric radiology in India. MATERIALS AND METHODS: A questionnaire was handed over to all attendees of a pediatric radiology conference to assess their opinion regarding the adequacy of pediatric training and practice in India. The questionnaire consisted of 10 multiple-choice and two descriptive questions. Descriptive statistical methods were used for analyzing the results. RESULTS: Eighty-one out of 400 delegates responded to the questionnaire. Among these 81 respondents, 50 (61.7%) felt that exposure to pediatric cases during postgraduate course was inadequate. Sixty-three out of 81 (77.7%) respondents thought that specialized training is required for practicing pediatric radiology, and 79 respondents (97%) felt that the number of such training programmes should increase. Forty-five out of 81 respondents (55.5%) were interested in pursuing pediatric radiology as a career. CONCLUSION: According to the opinion of the respondents of our survey, pediatric radiology remains an underdeveloped speciality in India. Considering the proportion of the population in the pediatric age and the poor health indicators in this age group, elaborate measures, as suggested, need to be implemented to improve pediatric radiology training and the care of sick children 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".