2018 International Consensus Meeting on Musculoskeletal Infection: Research Priorities from the General Assembly Questions
Bibliographic record
Abstract
Musculoskeletal infections (MSKI) remain the bane of orthopedic surgery, and result in grievous illness and inordinate costs that threaten healthcare systems. As prevention, diagnosis, and treatment has remained largely unchanged over the last 50 years, a 2nd International Consensus Meeting on Musculoskeletal Infection (ICM 2018, https://icmphilly.com) was completed. Questions pertaining to all areas of MSKI were extensively researched to prepare recommendations, which were discussed and voted on by the delegates using the Delphi methodology. The questions, including the General Assembly (GA) results, have been published (GA questions). However, as critical outcomes include: (i) incidence and cost data that substantiate the problems, and (ii) establishment of research priorities; an ICM 2018 research workgroup (RW) was assembled to accomplish these tasks. Here, we present the result of the RW consensus on the current and projected incidence of infection, and the costs per patient, for all orthopedic subspecialties, which range from 0.1% to 30%, and $17,000 to $150,000. The RW also identified the most important research questions. The Delphi methodology was utilized to initially derive four objective criteria to define a subset of the 164 GA questions that are high priority for future research. Thirty-eight questions (23% of all GA questions) achieved the requisite > 70% agreement vote, and are highlighted in this Consensus article within six thematic categories: acute versus chronic infection, host immunity, antibiotics, diagnosis, research caveats, and modifiable factors. Finally, the RW emphasizes that without appropriate funding to address these high priority research questions, a 3rd ICM on MSKI to address similar issues at greater cost is inevitable.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".