Frequency of in-office emergencies in primary care.
Bibliographic record
Abstract
OBJECTIVE: To quantify the frequency and types of in-office emergencies seen by FPs. DESIGN: A retrospective descriptive analysis of the frequency and types of in-office emergencies seen by FPs was done using the City of Ottawa Emergency Medical Services database. SETTING: Community medical offices in the Ottawa, Ont, region during a 3-year period (2004 to 2006). PARTICIPANTS: All patients for whom an ambulance was called to a medical office or clinic during the study period. MAIN OUTCOME MEASURES: Number of emergency calls from FPs' offices, primary complaints, seasonal variation, distance to the nearest emergency facility, and patients' demographic characteristics. RESULTS: A total of 3033 code 04 (life-threatening) emergency calls were received from FPs' offices during the study period. Demographic analysis of the calls showed that 91.3% of calls were regarding adult patients with an average age of 51.5 years. There was an overall statistically significant difference in the sex of the patients presenting (P < .001), but it was attributable to calls about genitourinary emergencies, which were almost all for women. The most common type of emergency reported was cardiovascular complaints. Of the 992 cardiovascular emergencies, 74.3% were complaints of ischemic chest pain. CONCLUSION: There is a great burden on the health care system from emergency calls, with continued unpreparedness from FPs. Clearly, FPs must take seriously the risk of being unprepared for in-office emergencies. Dissemination strategies must be developed so that the guidelines that have been developed can be effectively implemented in FP offices across the country.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".