Treatment of open fractures of the shaft of the tibia
Bibliographic record
Abstract
We have systematically reviewed the effect of alternative methods of stabilisation of open tibial fractures on the rates of reoperation, and the secondary outcomes of nonunion, deep and superficial infection, failure of the implant and malunion by the analysis of 799 citations on the subject, identified from computerised databases. Although 68 proved to be potentially eligible, only eight met all criteria for inclusion. Three investigators independently graded the quality of each study and extracted the relevant data. One study (n = 56 patients) suggested that the use of external fixators significantly decreased the requirement for reoperation when compared with fixation with plates. The use of unreamed nails, compared with external fixators (five studies, n = 396 patients), reduced the risk of reoperation, malunion and superficial infection. Comparison of reamed with unreamed nails showed a reduced risk of reoperation (two studies, n = 132) with the reamed technique. An indirect comparison between reamed nails and external fixators also showed a reduced risk of reoperation (two studies) when using nails. We have identified compelling evidence that unreamed nails reduced the incidence of reoperations, superficial infections and malunions, when compared with external fixators. The relative merits of reamed versus unreamed nails in the treatment of open tibial fractures remain uncertain.
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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".