Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Shape-matching technology provides preoperative three-dimensional templating of total knee arthroplasty (TKA) placement to restore optimal kinematics for the native prearthritic knee. Despite an accurate preoperative plan to construct cutting jigs for placement of the femoral and tibial implants in a TKA, it is hypothesized that the actual implementation or reproduction of this placement of implants remains suboptimal. Methods: A retrospective radiographic review of 67 primary unilateral TKAs performed using the OtisMed™ (OM, Stryker, Kalamazoo, MI) shape-matching technology was conducted to determine how closely the computer-generated OM preoperative plan resembled the postoperative femoral and tibial implant position in both the coronal and sagittal planes. Results: Preoperative and postoperative measurements were correlated in the coronal (r=0.407, P=0.001), but not the sagittal plane (r=−0.124, P=0.329). Postoperative coronal alignment differed from the preoperative plan more than 3° in 33.3% of cases, while sagittal alignment differed more than 3° in 56.3% of cases. Conclusions: A significant number of cases did not achieve precise placement of the implants per the OM plan, most notably in the sagittal plane. While these differences are noteworthy, the limiting factor still remains our ability to precisely execute the plan.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it