Fracture Table vs. Lateral Positioning for Intramedullary Fixation of Femur Fractures (The FLiP Study): A protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
Background: Femoral Shaft fractures are devastating and life threatening injuries. Femoral shaft fractures are most commonly treated with intramedullary fixation. Malrotation of the injured limb after fixation is a common and significant complication following femoral shaft fractures. During the operation, patients can be positioned either supine or in a lateral position. Additionally, patients can be placed on a standard radiolucent operating room table, or placed on a fracture table with traction statically applied to the operative limb throughout the case. Previous case series and cohort studies have shown equivalence between study groups, but choice between positioning options remains controversial. Methods: This represents a protocol for a randomized controlled pilot trial. We will be compared lateral positioning with use of manual traction to supine positioning with use of a fracture table. Primary outcomes will be in assessment for feasibility for a future full scale randomized trial, including evaluating patient recruitment, patient compliance with followup, contamination between treatment arms and others. Results: The primary outcome will be feasibility for a future trial. Secondary outcomes will include malrotation as measured through postoperative computed tomography scans and gait analysis at 6 months.
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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 | 0.043 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".