Bibliographic record
Abstract
OBJECTIVE: To report on a 30-year prospective study of deep infection in 1993 consecutive total hip arthroplasties performed by a single surgeon. METHODS: The relations of numerous variables to the incidence of deep infection were studied. RESULTS: The cumulative infection rate after the index total hip arthroplasties rose from 0.8% at 2 years to 1.4% at 20 years; 9.6% of the index operations required further surgery. When infections attributed to these secondary procedures were included, the infection rate rose from 0.9% at 2 years to 2% at 20 years. Although the usual variables increased the incidence of infection, the significant and most precise predictors of infection were radiologic diagnoses of upper pole grade III and protrusio acetabuli, an elevated erythrocyte sedimentation rate, alcoholism and units of blood transfused. CONCLUSION: From 2-20 years, the incidence of deep infection doubled. Preoperative recognition of the first 4 risk factors permits the use of additional prophylactic measures. Spinal or epidural anesthesia reduced the units of blood transfused (the fifth risk factor) and, hence, the risk of infection. Although most deep infections are seeded while the wound is open, there are many possible postoperative causes. In this study, fewer than one-third of the infections that presented after 2 years were related to hematogenous spread. The efficacy of clean air technology was supported, and it is recommended that all measures that may reduce the incidence of deep infection be employed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".