Assessing the Concordance of Trauma Registry Data and Hospital Records
Bibliographic record
Abstract
This study examined the concordance of trauma registry and hospital records in Queensland in 1998. The design involved a retrospective review of records and documentation comparison. Demographic variables from the registry were matched to hospital data to obtain admission/diagnoses data. There were four main types of error identified which included: failure to identify relevant patients, inappropriate inclusion of patients, insufficient/inaccurate data in hospital records, and insufficient/inaccurate data in the trauma registry. Of the 87 cases with data quality issues, 63% were due to Queensland Trauma Registry (QTR) data errors, 5% were due to hospital data errors, and in 32% of cases the source of errors was undetermined. Of the potential 1759 trauma cases from 1998, 12 cases should have been included in the registry that were not, 71 cases should not have been included in the registry, and 4 cases were removed from the study due to insufficient or inaccurate hospital record data. Overall, a concordance rate of approximately 95% was found between the trauma registry records and the hospital records.
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 | 0.029 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".