Bibliographic record
Abstract
Purpose: The purpose of this culminating project was first to conduct an extensive literature review of Nurse Practitioners (NPs) in orthopedic surgical settings, second to review the literature on how to create a successful professional poster, and then to present the findings of the orthopedic NP review in a professional poster. Background: Orthopedic conditions account for more disability, pain, and costs to the Canadian/American Healthcare systems than any other conditions. As a result patients are experiencing profound difficulty accessing orthopedic surgeons. As a solution to this shortage, NPs are becoming an essential part of the multidisciplinary orthopedic team in Level 1 trauma hospitals. Results: NPs are qualified and competent to work in a variety of orthopedic settings including preoperative clinics, primary care orthopedic clinics, as well as provide pre and postoperative care for patients within the hospital setting. The benefits of NPs in orthopedic surgical settings includes: increased access to care, improved team communication, decreased patient length of stay, improved quality of care, and improved patient satisfaction. Moreover, NPs meet patient needs while surgeons are operating, and have a positive impact on resident surgeon education. Conclusion: A need exists for NPs in orthopedic surgical settings in Canada to both improve access to healthcare for patients, and reduce the burden on orthopedic surgeons.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.052 |
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".