Interventional radiology and COVID-19: evidence-based measures to limit transmission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
T 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it