International Examination and Synthesis of the Primary and Secondary Surveys in Paramedicine
Bibliographic record
Abstract
Background To guide their care paramedics routinely rely upon two assessment and treatment algorithms, known as the primary survey and the secondary survey. No clear consensus of the concepts (assessments and interventions) that are, or should be, included in these algorithms exist internationally. Methods This paper evaluated Australasian paramedic clinical practice guidelines (CPGs), as well as six other international paramedic CPGs (USA, Ireland, UK, South Africa, Qatar, and the United Arab Emirates) in order to identify which concepts are currently described in best-practice recommendations for paramedics. The authors also contributed concepts that they felt were important additions based on their experience as veteran paramedics and paramedic educators. Results The resulting amalgamation of concepts identified in each term was then formed into two mnemonics which, together sequentially list approximately 100 specific clinical concepts that paramedics routinely consider in their care of patients. We describe these as the “International Paramedic Primary and Secondary Surveys”. Conclusion The primary and secondary surveys presented in this paper represent an evidence-based guide to the best practice in conducting a primary and secondary survey in the paramedic context. Findings will be of use to paramedics, paramedic students, and other clinicians working in remote or isolated practices.
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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 | 0.229 | 0.478 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".