Bibliographic record
Abstract
Over the past year, many of us have become involved in the development of strategic plans for the hospitals, health authorities and clinical practices in which we work in case an influenza pandemic occurs in the very near future. Planning for this on the front lines is difficult, due in large part to the uncertainties involved. How will the pandemic evolve? Is the current H5N1 strain of bird flu going to be ′the′ one? How closely will the pandemic resemble that of 1918? Will it have the same transmission characteristics as the yearly endemic influenza strains or will it be so different that our routine infection prevention precautions for influenza will be ineffective? Will there be a useful vaccine that is widely available and safe? That the current death rate associated with H5N1 strain infections in humans is approximately 60% is quite frightening; an influenza pandemic with such a high death rate is almost incomprehensible. Therefore, it is a relief to hear that the upper estimates are at a much lower rate of approximately 5% in most suggested epidemiological models. Will the use of oseltamivir really work to prevent infection, illness, morbidity or death? If so, will there be sufficient supplies available in Canada? How are we supposed to make plans so that our medical system, which is already quite stressed, will be functional under the extreme conditions that are anticipated? One major difficulty is that we do not actually know how soon, if at all, the pandemic will occur. Specific, highly detailed plans made today may not be applicable in the future. As a result, most contingency plans are being made for a generic situation based on the general assumption that some percentage of the workforce will be absent from work for some specified period of time. In general, the plans tend to be impersonal because they concentrate on essential functions that need to be undertaken in an institution and assume that, with training, all personnel can cross‐cover these services to accommodate for those times when the employees who routinely perform those tasks are absent.
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 | 0.000 | 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 | 0.000 |
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 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".