In-hospital cost comparison between the standard lateral and supercapsular percutaneously-assisted total hip surgical techniques for total hip replacement
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare the in-hospital costs associated with the tissue-sparing supercapsular percutaneously-assisted total hip (SuperPath) and traditional Lateral surgical techniques for total hip replacement (THR). METHODS: Between April 2013 and January 2014, in-hospital costs were reviewed for all THRs performed using the SuperPath technique by a single surgeon and all THRs performed using the Lateral technique by another surgeon at the same institution. RESULTS: Overall, costs were 28.4% higher in the Lateral group. This was largely attributable to increased costs associated with transfusion (+92.5%), patient rooms (+60.4%), patient food (+62.8%), narcotics (+42.5%), physical therapy (+52.5%), occupational therapy (+88.6%), and social work (+92.9%). The only costs noticeably increased for SuperPath were for imaging (+105.9%), and this was because the SuperPath surgeon performed intraoperative radiographs on all patients while the Lateral surgeon did not. CONCLUSIONS: The use of the SuperPath technique resulted in in-hospital cost reductions of over 28%, suggesting that this tissue-sparing surgical technique can be cost-effective primarily by facilitating early mobilisation and patient discharge even during a surgeon's initial experience with the approach.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".