Bibliographic record
Abstract
Conventional total knee arthroplasty aims to place the joint line perpendicular to the mechanical axis, despite the fact that the normal knee is inclined approximately 3 degrees, resulting in a medial proximal tibial angle of 87 degrees. The goal of a neutral mechanical axis is based largely on historical biomedical studies and the fact that it is easier to make a neutral tibial cut with conventional jigs and the eye. In order to balance the flexion and extension gap to accommodate a neutral tibial cut, in most patients, asymmetrical distal and posterior femoral cuts are required. The resulting position of the femoral component could be considered to be “mal-rotated” with respect to the patient9s soft tissue envelope. Soft tissue releases are often required. The target of neutral mechanical axis, or “straight and narrow,” represents a compromise position with respect to the kinematics of the knee. Neutral mechanical alignment may not confer any befits with respect to survivorship but dissatisfaction rates are high globally, with approximately 20% of patients being dissatisfied after total knee arthroplasty in multiple studies. Computer assisted surgery and shape matching allow for consideration of placing total knee components to match an individual9s anatomy, as opposed to forcing the knee into an unnatural neutral mechanical alignment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".