Bibliographic record
Abstract
Few viruses have shaped the course of human history more than influenza viruses. A century since the 1918-1919 Spanish influenza pandemic-the largest and deadliest influenza pandemic in recorded history-we have learned much about pandemic influenza and the origins of antigenic drift among influenza A viruses. Despite this knowledge, we remain largely underprepared for when the next major pandemic occurs.While emergency departments are likely to care for the first cases of pandemic influenza, intensive care units (ICUs) will certainly see the sickest and will likely have the most complex issues regarding resource allocation. Intensivists must therefore be prepared for the next pandemic influenza virus. Preparation requires multiple steps, including careful surveillance for new pandemics, a scalable response system to respond to surge capacity, vaccine production mechanisms, coordinated communication strategies, and stream-lined research plans for timely initiation during a pandemic. Conservative models of a large-scale influenza pandemic predict more than 170% utilization of ICU-level resources. When faced with pandemic influenza, ICUs must have a strategy for resource allocation as strain increases on the system.There are several current threats, including avian influenza A(H5N1) and A(H7N9) viruses. As humans continue to live in closer proximity to each other, travel more extensively, and interact with greater numbers of birds and livestock, the risk of emergence of the next pandemic influenza virus mounts. Now is the time to prepare and coordinate local, national, and global efforts.
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".