Poor Citation of Prior Evidence in Hip Fracture Trials
Bibliographic record
Abstract
BACKGROUND: Failure to cite prior evidence in the medical literature may result in publication redundancy and inefficient use of research funding. We evaluated trials in which internal fixation was compared with arthroplasty for the treatment of hip fractures in order to determine the extent to which these randomized trials cited all relevant previous trials. METHODS: We searched MEDLINE and Embase for all relevant articles on four topics: internal fixation compared with arthroplasty, total hip arthroplasty compared with hemiarthroplasty, sliding hip screws compared with other fixation devices, and surgical delay of hip fracture treatment. We determined the proportion of previous studies that were cited in comparison with the total number of previous studies that were citable (i.e., the citation rate) as well as the proportion of times that a study was cited in comparison with the total number of times that it could have been cited (i.e., the hit rate). A cumulative meta-analysis was performed for the "internal fixation compared with arthroplasty" topic to determine whether compelling evidence favoring one intervention existed at an earlier time. RESULTS: In total, sixty studies were assessed and yielded an overall citation rate of 48%. All "highly cited" studies reported a positive result (favoring arthroplasty), and 60% were published in The Journal of Bone and Joint Surgery (American or British volume). The results of a study and the journal of publication significantly affected the hit rate (p < 0.05). CONCLUSIONS: Our review of studies of hip fracture treatment suggests poor citation of the previous literature. Studies in higher-impact journals with positive results are more likely to be cited in subsequent studies. Therefore, redundancy in publication and unnecessary surgical trials often occur.
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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 | 0.350 | 0.777 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.066 | 0.062 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".