Bibliographic record
Abstract
To the Editor The current Ebola epidemic, the largest in history, has to date affected primarily African nations. However, several cases were diagnosed in the United States, including 3 involving health care workers.1 The spread of Ebola to North America and the resulting risk to health care workers bear striking similarities to the 2003 outbreak of severe acute respiratory syndrome (SARS), which originated in Taipei, Taiwan, and quickly spread to other countries. In Toronto, Canada, the epidemic resulted in 438 probable or suspected cases of SARS. More than half of these cases involved health care workers, including 3 of the 43 SARS-related deaths in Toronto.2 Indeed, more than half of SARS cases in both Toronto and Taiwan were infections related to the provision of health care.2,3 In particular, critical care providers and anesthesiologists, especially those involved in airway procedures such as endotracheal intubation, were at the highest risk of infection because the primary mode of transmission was through contact of respiratory droplets with mucous membranes.2 A detailed review of the experiences of health care workers showed that frontline workers directly involved in patient care had limited opportunity to rapidly inform policy makers about their concerns and suggestions. Indeed, in the face of an acute and evolving epidemic, the serious practical challenges of developing and modifying guidelines cannot be underestimated. During the SARS epidemic, safety protocols were typically developed by infection control experts who lacked clinical expertise dealing with critically ill patients or by clinicians who lacked experience treating patients with SARS.2 Communication was inconsistent and confusing, and information came from multiple sources, including local hospitals and national and international agencies. Through this letter, we wish to share the lessons we learned in hopes of helping others avoid similar mistakes during the current Ebola epidemic. As reported in a 2006 article, we identified all frontline health care workers who had performed intubation with SARS-infected patients.2 Of the 59 health care workers who had performed at least 1 such intubation, 33 consented to an interview. Within this group, 3 (13%) of the 23 health care workers who performed intubation during the first wave of SARS (February 23 to April 21, 2003) became infected, whereas none of the 10 health care workers who performed intubation during the second wave (from April 22 to July 1, 2003) became infected. This striking reduction in incidence presumably resulted from increased use of isolation precautions and implementation of simple and practical management guidelines, proposed at least, in part, by frontline health care workers. For example, it was recommended that droplet spread could be minimized by using a paralytic agent for intubation. In addition, it was recommended that intubation should be performed by the most experienced health care workers available, to reduce the time and number of attempts required. Early airway intervention was beneficial, and close proximity of airway tools was necessary, with personnel immediately available to assist.2Table 1: Infectious Disease Risk Management FrameworkThese descriptions of personal experiences were used to develop a risk management framework that could rapidly integrate the experiences of health care workers into guidelines and recommendations for use during future outbreaks of infectious diseases. The analysis showed potential areas of weakness or vulnerability, termed “breakpoints,” in processes, people, tools, and infrastructure. Using the grounded theory approach and the data from our interviews with frontline health care workers, we identified recommendations to mitigate the spread of future disease outbreaks. The risk management framework, shown in Table 1, is highly relevant to the current Ebola epidemic, and it is our hope that the lessons learned during the SARS epidemic a decade ago will not be forgotten. In advance of a major outbreak, bidirectional communication systems must be established and risk management tools adopted to limit the spread of Ebola to health care workers and others. Karen C. Nanji, MD, MPH Department of Anesthesia, Critical Care and Pain Medicine Massachusetts General Hospital Department of Anaesthesia Harvard Medical School Boston, Massachusetts [email protected] Beverley A. Orser, MD, PhD Department of Anesthesia Sunnybrook Health Sciences Center Toronto, Ontario, Canada Department of Anesthesia University of Toronto Toronto, Ontario, Canada
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".