Cost-reduction Analysis of Percutaneous Pinning of Hand Fractures in an Outpatient Clinic
Bibliographic record
Abstract
Background: The University of Sherbrooke's Hospital Center operating room has been affected by the COVID-19 pandemic, prompting surgeons to seek alternative ways to treat acute injuries requiring surgery. In the spring of 2020, we began performing percutaneous pinning of hand fractures in our outpatient clinic. We aimed to estimate the savings in 2021 by transferring these procedures from the operating room to the outpatient clinic. Methods: We identified all patients with hand injuries who received percutaneous pinning in 2021 using billing codes. Only patients treated in the outpatient clinic were included. We estimated the cost of hand fracture fixation in the operating room by considering the anesthesiologist's fee, the hospital's hourly rate for a 1-hour surgery (including a respiratory therapist, 2 nurses, and equipment) and salary bonuses for unfavorable hours, subtracting the cost difference of outpatient equipment. Results: We identified 114 patients treated with percutaneous pinning, of whom 93 were included in our study. Our calculations showed a total cost reduction of CAD $55,789 in 2021. Conclusions: Percutaneous pinning of hand fractures in an outpatient setting resulted in a yearly cost reduction of more than CAD $55,000. Investing in ambulatory care for hand fracture management benefits both patients and institutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".