Erratum to: No Difference Between Trabecular Metal Cones and Femoral Head Allografts in Revision TKA: Minimum 5-year Followup
Bibliographic record
Abstract
The authors of the published study, “No Difference Between Trabecular Metal Cones and Femoral Head Allografts in Revision TKA: Minimum 5-year Followup” would like to correct the following errors found in their paper. In the Patients and Methods section, the percentages in the sentence describing the number of femoral head allografts and trabecular metal cones among 45 TKA revisions are not correct. Femoral head allograft was used in 30 TKA revisions (66.7%, not 75%) and trabecular metal cones were used in 15 TKA revisions (33.3%, not 25%). Additionally, in the Trabecular Metal Cone Technique section, there is a discrepancy between the data presented in the text and Table 1. The data presented in Table 1 is incorrect. A total of four Type 2B and two Type 3 femoral defects, and two Type 2A and seven Type 2B tibial defects were treated using trabecular metal cones. The corrected Table 1 is below.Table 1.: Patient demographics*.The authors apologize for the errors.
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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".