Analyzing the Efficacy of Pain Management and Continuous Infusion Regional Anesthetic Catheters in the Orthopaedic Setting
Bibliographic record
Abstract
My name is Ulises Pantaleon Rodriguez and I am a fourth year nursing student currently finishing my integrative practicum and final consolidation on the orthopaedic inpatient surgery floor at University Hospital in London, Ontario. I have had prior placements within this field of nursing and have been drawn towards orthopaedic placements due to my interest in sports medicine and the musculoskeletal system of the body. Through multiple orthopaedic placements I have witnessed first-hand the crippling effects of pain on patients, causing both physical and psychological distress. As a result I have encountered a multitude of different pain management control techniques ranging from pharmacological to non-pharmacological in nature. Through my experience on the orthopaedic inpatient surgery floor, I encountered the use of continuous infusion regional anesthetic catheters, commonly known as nerve block catheters. This innovative pain management technique yielded a plethora of different results for different patients, which sparked my interest into the relevant research and literature on their use. Pain management is one of the most important aspects of nursing care and the health care field as a whole, and it is my belief that healthcare practitioners must remain knowledgeable and educated on the latest techniques.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".