Arthroplasty Options for the Young Patient
Bibliographic record
Abstract
Our purpose was to determine whether metal femoral heads scratch with in vivo use, to characterize the scratching that occurs, and to determine whether this scratching affected polyethylene wear. Assessment of 133 consecutive retrieved femoral heads showed that metal femoral heads do scratch with in vivo use, that cobalt-chromium femoral heads are more scratch resistant than titanium alloy heads, and that scratching seems to be time dependent. Profilmetry studies showed that all roughness parameters (average roughness, maximum peak to lowest valley distance, mean peak height above the mean surface line, estimate of small peaks above the main plateau of the surface, and estimate of the depth of the valleys below the mean plateau of the surface with the exception of the symmetry of the profile about its mean line) showed increased roughness with time of use. Cobalt-chromium and Oxinium femoral heads were damaged in a dislocation model. Assessment of these femoral heads in a wear simulator revealed that against conventional polyethylene, a damaged Oxinium femoral head had no more wear than a new cobalt-chromium articulation on the same polyethylene (36.5/million cycles versus 38.4 mm/million). Against cross-linked polyethylene, a damaged Oxinium femoral head had minimal wear (1.5 mm cubed per Mc).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".