Path Analysis of Factors for Delayed Healing and Nonunion in 416 Operatively Treated Tibial Shaft Fractures
Bibliographic record
Abstract
UNLABELLED: A prospective observational study was done in 41 trauma centers. Four hundred sixteen patients with tibial shaft fractures were treated operatively and followed up for at least 6 months. Fifty-two (13%) cases of delayed healing or nonunion were reported. In such nonrandomized observational studies, multiple interrelationships exist between prognostic factors and patient outcomes. We used path analyses to investigate prognostic factors associated with the occurrence of delayed healing or nonunion. The most important factors were identified using multivariate regression analyses, and interrelationships between factors were illustrated using a path diagram. Fractures with open injuries less than and greater than 5 cm were 3.6 and 5.7 times as likely, respectively, to have delayed healing or nonunion as fractures with no skin injuries. The Müller-AO classification of fractures did not provide additional prognostic information. The risk of healing problems was doubled for fractures of the distal shaft and for fractures showing a postoperative diastasis. Treatment options showed an indirect effect on outcome with the occurrence of diastasis. A model for predicting delayed healing or nonunion is proposed. We encourage the use of path analysis in orthopaedics as a powerful visual technique to interpret data from observational studies. LEVEL OF EVIDENCE: Prognostic study, Level II-1 (retrospective study). See the Guidelines for Authors for a complete description of levels of evidence.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".