Medical radiation countermeasures for nuclear and radiological emergencies: Current status and future perspectives
Bibliographic record
Abstract
Nuclear and radiological emergencies (NREs) occurred globally and recent incidences in India are indicating toward the need for comprehensive medical preparedness required both at incident site and hospitals. The enhanced threat attributed toward insurgency is another causative factor of worry. The response capabilities and operational readiness of responders (both health and non-health service providers) in contaminated environment need to be supported by advancement in R & D and technological efforts to develop prophylactics and radiation mitigators. It is essential to develop phase 1 alternatives of such drugs for unseen threats as a part of initial preparedness. At the incident site and hospital level, external decontamination procedures need to be standardized and supported by protective clothing and Shudika kits developed by INMAS. The medical management of exposure requires systematic approach to perform triage, resuscitation and curative care. The internal contamination requires decorporation agents to be administered based on procedural diagnostics. Various key issues pertaining to policy decisions, R & D promotion, community awareness, specialized infrastructure for NREs preparedness has been discussed. The present review is an attempt to provide vital information about the current status of various radiation countermeasures and future perspective(s) ahead.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".