Controlling droplet-transmitted respiratory infections: best practices and cost.
Bibliographic record
Abstract
OBJECTIVE: To promote incorporation of new guidelines on control of respiratory infections into family physicians' practices. SOURCES OF INFORMATION: The World Health Organization website on pandemic influenza, the Canadian Pandemic Influenza Plan, the Ontario guidelines on respiratory infection control, and research on implementing guidelines into family practice were reviewed. We also researched and calculated what the costs of implementing the guidelines would be. MAIN MESSAGE: Effective control of respiratory infections in physicians' offices can be achieved by displaying signs in the waiting room, having reception staff give masks to patients with cough and fever, instructing these patients to clean their hands with alcohol gel and to sit at least 1 m from others, inquiring about patients' or their close contacts' recent travel, using disinfectant wipes to clean possibly contaminated surfaces in waiting rooms and examining areas, and having staff and care providers wear masks and wash hands or use alcohol gel. The approximate annual cost of incorporating the guidelines is about 800 dollars per physician. CONCLUSION: Because the outbreak of an influenza pandemic is likely imminent, implementing standard guidelines for control of respiratory infections in primary care offices seems wise. Following these guidelines would help prevent patients and staff from contracting serious respiratory illnesses.
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 | 0.012 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".