Experience with Registered Nurse First Assistants (RNFA) vs General Practitioner Surgical Assistants (GPSA) in Bilateral Breast Reduction
Bibliographic record
Abstract
Introduction: In a community setting, a surgical assistant for breast reduction surgery will either be a Registered Nurse First Assistant (RNFA) or a General Physician Surgical Assistant (GPSA). The two groups of assistants are compared in this study. Methods: A retrospective review over a two-year period was performed and evaluated the surgical outcomes of 2 groups of 20 cases of breast reductions, one where the primary assistant was an RNFA and the other with a GPSA. Patient satisfaction was determined through a self-reporting survey beyond the six-month postoperative period, and a complication profile was noted for each patient. Results: Descriptive data for the GPSA group of 20 bilateral breast reductions and the data for the 20 bilateral breast reduction patients operated upon with an RNFA surgical assistant was gathered. The data was analyzed using a Student’s t-test to compare the averages of different parameters in the two groups and determine if the differences between them were significant. The results were comparable in both groups with respect to age, size of bilateral breast reduction, body mass index (BMI) and other parameters. However, statistically significant differences (p<.05) were noted between the two groups for operative time and estimated blood loss, both of which were in favor of the RNFA group. Conclusion: A statistically significant difference in operative time and estimated blood loss were noted in the group of patients operated upon with the assistance of a Registered Nurse First Assistant (RNFA). A cost savings to Ontario’s Ministry of Health and Long-Term Care (MOHLTC) was also realized by utilizing an RNFA over a GPSA. The cost-effectiveness of an RNFA, their comprehensive training, nursing background and flexibility make them a valuable asset to any plastic surgery team and should be considered by hospitals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".