Unicondylar knee arthroplasty in the inpatient vs. outpatient setting: Impact on process time
Bibliographic record
Abstract
Objective: There is a lack of research on the impact of transitioning inpatient procedures to the outpatient setting, specifically on process time. Unicondylar knee arthroplasty (UKA) presents an opportunity for further investigation as it is already in the early stages of transitioning to the outpatient setting.Methods: This study analyzed the medical records of 1,075 patients who received UKA from a single surgeon (400 in the outpatient setting and 675 in the inpatient setting). Time in Pre-Op, surgery time, and time in post-anesthesia care unit (PACU) were recorded and compared between inpatient and outpatient settings using Ordinary Least Squares Regression models.Results: Outpatient UKAs outperformed inpatient UKAs across two out of three process time variables even after controlling for comorbidities, social history, demographics, and surgery related characteristics. Actual surgery time was no different between the two settings.Conclusions: This study demonstrated that UKA performed in the outpatient setting is associated substantial time savings preoperatively and postoperatively compared with cases performed in the inpatient setting. More research is needed to compare other outcome measures such as patient outcomes of UKA between the two settings. Implications beyond time savings should consider supply and human resources costs.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".