The Role of SIGN in the Development of a Global Orthopaedic Trauma Database
Bibliographic record
Abstract
BACKGROUND: The global burden of injury is receiving recognition as a major public health problem but inadequate information delays many proposed solutions. Many attempts to collect reliable data on orthopaedic trauma have been unsuccessful. The Surgical Implant Generation Network (SIGN) database is one of the largest collections of fracture cases from lower and middle income countries. QUESTIONS/PURPOSES: We describe the information in the SIGN database then address two questions: In the context of the design and implementation of a global trauma database, what lessons does the SIGN database teach? Does the SIGN program have a role in the evolution of a wider global system? METHODS: The SIGN database is Internet based. After treating a patient with a SIGN nail surgeons enter radiographs and details of the case. RESULTS: Over 26000 cases are in the SIGN database. The database has been used as a source of cases for followup studies. Analysis shows the data are of sufficient quality to allow studies of fracture patterns but not for outcome studies or bone measurement. WHERE DO WE NEED TO GO?: A global database with more comprehensive coverage of injuries, causes, treatment modalities and outcomes is needed. HOW DO WE GET THERE?: The SIGN database itself will not become a global trauma database (GTD) but the personnel of the SIGN program have much to offer in the design and adoption of a GTD. Studies of suitable methods of data collection and the incentive to use them are required.
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 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 | 0.214 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.027 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".