Abstracting Data from Medical Examiner/Coroner Reports: Concordance among Abstractors and Implications for Data Reporting
Bibliographic record
Abstract
The purpose of this study was two-pronged: 1) to determine the level of concordance (agreement) between multiple records abstractors who extracted defined data elements from printed medical examiner/coroner (ME/C) death investigation records; and 2) to identify data items for which improved reporting could facilitate the effective use of ME/C reports and data. Four hundred ninety four printed death investigation records were obtained from 224 medical examiner/coroner offices throughout the United States. Trained abstractors were asked to extract information for 110 data elements from investigative reports. Additional data elements for each toxicology workup were abstracted from toxicology laboratory reports and six-digit AIS codes were also abstracted for each injury as described in autopsy reports. The ability of multiple abstractors to identify each data element and identically abstract the data was assessed using Kappa statistical methods. Level of agreement for many data elements was very good (>0.9), but for some data elements agreement was marginal to poor, especially for items related to toxicology, the nature of specific injuries, and dates, times of the occurrence of death and injury. Many data items can be easily abstracted from ME/C records. However, some data items seem difficult to abstract reliably in all cases. Standardizing the report formats used by ME/Cs and/or standardizing the electronic storage of ME/C data would make the abstraction of such data easier and improve the usefulness of ME/C data.
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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 | 0.468 | 0.769 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 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; 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".