Teriparatide in the Treatment of a Loose Hip Prosthesis
Bibliographic record
Abstract
To the Editor: The complexity of surgical and medical problems associated with reimplantation of a loose hip prosthesis does not always render this procedure acceptable. In addition, certain drugs may enhance the quality of bone around the prosthesis1,2. We treated a patient with teriparatide following loosening of his hip prosthesis, which was implanted for the second time; further surgery was no longer an option. The patient had osteoarthritis and had an uncemented prosthesis when he was 56 years of age; by the age of 61 years he required a reimplant, as a result of aseptic loosening. At the age of 74 years, he began to develop increasing pain and progressive loss of power. He had a history of dilated cardiomyopathy, and in view of this a surgical solution was ruled out. By the age of 77 years, he was treated with clodronate (100 mg intramuscularly weekly) and oral calcium (1.5 g daily) for 1 year. At age 78, radiographic imaging showed extensive loosening of the bone around the cup and a marked reduction in cortical thickness. Although he was already using a cane, he was advised to use 2 canes to allow partial weight-bearing. He was subsequently prescribed teriparatide (20 μg daily for … [↵][1]Address correspondence to Dr. D. Sarti, Via Mario Conti 53, San Lazzaro di Savena, Bologna, 40068, Italy. E-mail: danielesarti1978{at}gmail.com [1]: #xref-corresp-1-1
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".