The effect of NSAIDs on postfracture bone healing: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
To determine whether nonsteroidal anti-inflammatory drugs (NSAIDs) have an adverse effect on bone healing by evaluating all available human randomized controlled trials (RCTs) on this subject. DATA SOURCES: A comprehensive search of electronic databases (PubMed, MEDLINE, and Cross-References) until October 2018 comparing the occurrence of nonunion in patients who received NSAIDs to the control group through RCTs. STUDY SELECTION: Inclusion criteria were English-only studies, and the type of studies was restricted to RCTs. DATA EXTRACTION: Two authors independently extracted data from the selected studies, and the data collected were compared to verify agreement. DATA SYNTHESIS: Nonunion was the main outcome evaluated in each study. Regression analysis was used to estimate the relative risk comparing the duration and the type of NSAIDs by calculating the odds ratio (OR) for dichotomous variables. Studies were weighed by the inverse of the variance of the outcome, and a fixed-effects model was used for all analyses. CONCLUSIONS: Six RCTs (609 patients) were included. The risk of nonunion was higher in the patients who were given NSAIDs after the fracture with an OR of 3.47. However, once the studies were categorized into the duration of treatment with NSAIDs, those who received NSAIDs for a short period (<2 weeks) did not show any significant risk of nonunion compared to those who received NSAIDs for a long period (>4 weeks). Indomethacin was associated with a significant higher nonunion rate and OR ranging from 1.66 to 9.03 compared with other NSAIDs that did not show a significant nonunion risk.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".