Relationship Between Declared Funding Support and Level of Evidence
Bibliographic record
Abstract
BACKGROUND: The relationship between industry and the orthopaedic community is under increasing scrutiny. Industry traditionally has funded a substantial amount of the orthopaedic research published in this and other journals. The objective of the present study was to investigate associations between the level of evidence and declared source(s) of funding in papers published in the American volume of The Journal of Bone and Joint Surgery. METHODS: All articles published in the American volume of The Journal of Bone and Joint Surgery from January 2003 to December 2007 were reviewed by a single individual. Primary research papers with an assigned level of evidence were assessed with regard to source of funding, subject area, and results. The association between source of funding and level of evidence was described with use of contingency tables and chi-square tests. RESULTS: Of 886 studies with an assigned level of evidence, 246 were funded by industry, of which 124 (50%) were graded as Level-IV evidence. Among 274 studies funded by governments, foundations, or universities, only seventy-nine (29%) were graded as Level-IV evidence. Among 366 studies with no funding declared, 209 (57%) were graded as Level-IV evidence. The association between industry funding and a lower level of evidence was significant (p < 0.0001). CONCLUSIONS: While industry funded a larger number of studies than any other single source in this journal, the level of evidence of industry-funded studies was lower that that for studies funded by governments, foundations, or universities. Improving the scientific quality of industry-funded research might increase the quality of evidence for making orthopaedic decisions.
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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 | 0.179 | 0.713 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".