Practical lessons from the first outbreaks: Clinical presentation, obstacles, and management strategies for severe pandemic (pH1N1) 2009 influenza pneumonitis
Bibliographic record
Abstract
During the initial spring wave of novel influenza pH1N1 (2009), several North American cities experienced localized epidemics that served as a harbinger of the larger second Fall wave of infection. The city of Winnipeg, the capital of the province of Manitoba in central Canada, was one of the first in North America to deal with a rapid presentation of large numbers of patients requiring critical care services resulting from pandemic (pH1N1) 2009 influenza-associated respiratory failure. Mexico City, Orlando, FL, and Salt Lake City, UT, were other Northern Hemisphere sites of heavy disease activity during the spring wave of the pandemic. This article is written in a narrative format that allows the reader to understand the problems (both major and mundane, anticipated and unexpected) experienced by healthcare workers in these sites during this pandemic. Descriptions cover a range of issues and difficulties that caused significant stress to the operations of intensive care units in these cities. We hope to offer some insight into potential pitfalls and problems that may be experienced by other centers and provide some potential approaches to addressing these issues.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".