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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".