An evidence-based approach to reducing bed rest in the invasive cardiology patient population
Bibliographic record
Abstract
In 2003, the Honour Society of Nursing, Sigma Theta Tau International and Nursing Spectrum sponsored an “Innovations in Clinical Excellence” contest to recognise exemplars of evidence-based nursing practice. The following 5 papers are winning entries, which are published with permission of the Honour Society of Nursing, Sigma Theta Tau International. The most uncomfortable part of hospital admission for patients requiring coronary interventional and/or diagnostic procedures is the time required to lie flat after removal of the indwelling femoral arterial introducer sheath. Conventional practice required a minimum of 6 hours of supine bed rest after sheath removal, often resulting in the problem of back pain. The Nurse Practitioner/Clinical Nurse Specialist (NP/CNS) for interventional cardiology targeted this problem for further investigation in 1994 and took the lead in determining the process and strategies to be used. A group of interested physicians and nurses was convened, reflecting the multidisciplinary interest in addressing this clinical problem. The basis for the practice of prolonged bed rest was a mix of ritual, research, and expert opinion. The expert consensus was that prolonged bed rest was required to ensure adequate haemostasis at the femoral arterial puncture site. Research, much of it conducted by nurse researchers, had been gradually demonstrating the safety of …
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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 | 0.056 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| 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".