Protected Code Stroke
Bibliographic record
Abstract
Background and Purpose- Hyperacute assessment and management of patients with stroke, termed code stroke, is a time-sensitive and high-stakes clinical scenario. In the context of the current coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus, the ability to deliver timely and efficacious care must be balanced with the risk of infectious exposure to the clinical team. Furthermore, rapid and effective stroke care remains paramount to achieve maximal functional recovery for those needing admission and to triage care appropriately for those who may be presenting with neurological symptoms but have an alternative diagnosis. Methods- Available resources, COVID-19-specific infection prevention and control recommendations, and expert consensus were used to identify clinical screening criteria for patients and provide the required nuanced considerations for the healthcare team, thereby modifying the conventional code stroke processes to achieve a protected designation. Results- A protected code stroke algorithm was developed. Features specific to prenotification and clinical status of the patient were used to define precode screening. These include primary infectious symptoms, clinical, and examination features. A focused framework was then developed with regard to a protected code stroke. We outline the specifics of personal protective equipment use and considerations thereof including aspects of crisis resource management impacting team role designation and human performance factors during a protected code stroke. Conclusions- We introduce the concept of a protected code stroke during a pandemic, as in the case of COVID-19, and provide a framework for key considerations including screening, personal protective equipment, and crisis resource management. These considerations and suggested algorithms can be utilized and adapted for local practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".