Lithium for Fracture Treatment (LiFT): a double-blind randomised control trial protocol
Bibliographic record
Abstract
INTRODUCTION: Fracture healing can fail in up to 10% of cases despite appropriate treatment. While lithium has been the standard treatment for bipolar disorder, it may also have a significant impact to increase bone healing in patients with long bone fractures. To translate this knowledge into clinical practice, a randomised clinical trial (RCT) is proposed. METHODS AND ANALYSIS: A multicentre double blind, placebo-controlled RCT is proposed to evaluate the efficacy of lithium to increase the rate and predictability of long bone fracture healing in healthy adults compared to lactose placebo treatment. 160 healthy individuals from 18 to 55 years of age presenting with shaft fractures of the femur, tibia/fibula, humerus or clavicle will be eligible. Fractures will be randomised to placebo (lactose) or treatment (300 mg lithium carbonate) group within 2 weeks of the injury. The primary outcome measure will be radiographic union defined as visible callus bridging on three of the four cortices at the fracture site using a validated radiographic union score. Secondary outcome measures will include functional assessment and pain scoring. ETHICS AND DISSEMINATION: Participant confidentiality will be maintained with publication of results. Research Ethics Board Approval: Sunnybrook Research Institute (REB # 356-2016). Health Canada Approval (HC6-24-C201560). Results of the main trial and secondary endpoints will be submitted for publication in a peer-reviewed journal and presented at conferences. TRIAL REGISTRATION NUMBER: NCT02999022.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.095 | 0.019 |
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".