Interventional radiology and COVID-19: evidence-based measures to limit transmission
Bibliographic record
Abstract
he ongoing COVID-19 outbreak caused by a novel Corona virus known as SARS-CoV-2, has become a global pandemic with more than 270 000 cases reported worldwide at the time of this article, with number of deaths more than 11 000.With the pathogen being a novel virus, many aspects of the organism and manifestations related to acute and long-term consequences are still unknown.The virus characteristics, mutagenic forms, origin and routes of animal to human transmission, mode of human spread, extent of asymptomatic carriers, variables affecting mortality, effective treatment options and feasibility of developing vaccine are all parameters which need further study and definition.As other departments, it is imperative on Interventional Radiology (IR) to provide its services safely and effectively while reducing the risk of transmission to the staff.The virus has been shown to have phylogenetic similarity as well as severity of manifestations comparable to severe respiratory syndrome (SARS) caused by SARS-CoV-1.With much more yet to be known about the virus, an adequate protocol needs to be derived from the available fragmentary data and lessons learnt from prior outbreaks like SARS.We aim to put forth guidelines that the service needs to adopt to maintain a balance between optimal patient care without compromising on precautionary measures for IR staff. Vetting and prioritizationThe risk of transmission is directly related to the degree of contact between the public and HCP.Therefore, interventional radiologists are predisposed to a substantial risk of ac-ABSTRACT As we face an explosion of COVID-19 cases and deal with an unprecedented set of circumstances all over the world, healthcare personnel are at the forefront, dealing with this emerging scenario.Certain subspecialties like interventional radiology entails a greater risk of acquiring and transmitting infection due to the close patient contact and invasive patient care the service provides.This makes it imperative to develop and set guidelines in place to limit transmission and utilize resources in an optimal fashion.A multi-tiered approach needs to be devised and monitored at the administrative level, taking into account the various staff and patient contact points.Based on these factors, work site and health force rearrangements need to be in place, while enforcing segregation and disinfection parameters.We are putting forth an all-encompassing review of infection control measures that cover the dynamics of patient care and staff protocols that such a situation demands of an interventional department.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".