Dealing with office emergencies. Stepwise approach for family physicians.
Bibliographic record
Abstract
OBJECTIVE: To develop a simple stepwise approach to initial management of emergencies in family physicians' offices; to review how to prepare health care teams and equipment; and to illustrate a general approach to three of the most common office emergencies. QUALITY OF EVIDENCE: MEDLINE was searched from January 1980 to December 2001. Articles were selected based on their clinical relevance, quality of evidence, and date of publication. We reviewed American family medicine, pediatric, dental, and dermatologic articles, but found that the area has not been well studied from a Canadian family medicine perspective. Consensus statements by specialty professional groups were used to identify accepted emergency medical treatments. MAIN MESSAGE: Family medicine offices are frequently poorly equipped and inadequately prepared to deal with emergencies. Straightforward emergency response plans can be designed and tailored to an office's risk profile. A systematic team approach and effective use of skills, support staff, and equipment is important. The general approach can be modified for specific patients or conditions. CONCLUSION: Family physicians can plan ahead and use a team approach to develop a simple stepwise response to emergency situations in the office.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".