Review of emergency preparedness in the office setting: How best to prepare based on your practice and patient demographic characteristics.
Bibliographic record
Abstract
OBJECTIVE: To outline an approach to assessing the risk of emergencies in one's medical practice and determining the equipment and medications required for emergencies and the necessary staff training to meet this important facet of patient care. SOURCES OF INFORMATION: The emergency preparedness recommendations presented in this article are based on data collected from family physicians' current preparedness plans, formal physician evaluation and informal feedback provided after 2 large group presentations, and the authors' expertise in areas including family medicine, emergency medicine, prehospital care, and pharmacology. MAIN MESSAGE: Delineating risk based on practice profile, location, and demographic characteristics will inform the development of an appropriate plan to meet both public expectations and professional obligations. Reviewing the plan or having a practice drill of the plan once developed will improve the process in the event of an emergency. It is also essential to have medication and equipment checked periodically for expiry dates and proper functioning. CONCLUSION: Physicians will encounter office emergencies at some time in their practice. Appropriate risk assessment, planning, and preparedness will allow the provision of high-quality care, safety for staff members, the best patient outcomes, and the reward of having managed a time-sensitive problem in an efficient and effective manner.
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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".