Complications Following Management of Displaced Intra-Articular Calcaneal Fractures: A Prospective Randomized Trial Comparing Open Reduction Internal Fixation With Nonoperative Management
Bibliographic record
Abstract
OBJECTIVE: To report on all complications experienced by patients with displaced intra-articular calcaneal fractures (DIACFs) following nonoperative management or open reduction internal fixation (ORIF). DESIGN: Prospective, randomized, multicenter study. SETTING: Four level I trauma centers. PATIENTS: The patient population consisted of consecutive patients, age 17 to 65 at the time of injury, presenting to 1 of the centers with DIACFs between April 1991 and December 1998. INTERVENTIONS: Patients were randomized to the nonoperative treatment group or to operative reduction using a lateral approach to the calcaneus. MAIN OUTCOME MEASUREMENTS: Follow-up for patients was at 2 weeks, 6 weeks, 3 months, 12 months, 24 months, and once greater than 24 months following injury. At each follow-up interval, patients were assessed for the development of major and minor complications. After a minimum of 2-year follow-up, patients were asked to fill out a validated visual analogue scale questionnaire (VAS) and a general health review (SF-36). RESULTS: There were 226 DIACFs (206 patients) in the ORIF group with 57 of 226 (25%) fractures (57 of 206 patients [28%]) having at least 1 major complication. Of 233 fractures (218 patients) nonoperatively managed, 42 (18%) (42 of 218 patients [19%]) developed at least 1 major complication (indirectly resulting in surgery). CONCLUSION: Complications occur regardless of the management strategy chosen for DIACFs and despite management by experienced surgeons. Complications are a cause of significant morbidity for patients. Outcome scores in this study tend to support ORIF for calcaneal fractures. However, ORIF patients are more likely to develop complications. Certain patient populations (WCB and Sanders type IV) developed a high incidence of complications regardless of the management strategy chosen.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".