Bibliographic record
Abstract
The medical examiner's office in Broward County is responsible for determining the cause and manner of death in cases falling under its jurisdiction and issuing death certificates on these decedents. Amendments are occasionally required to correct misinformation on death certificates or within the autopsy reports. The purpose of this study was to investigate the major causes for the amendments and to develop strategies to avoid future errors. We found 128 cases from 2006 to 2007 that required amendments; 103 contained sufficient data in the file for further analysis. Over this time period, 3790 death certificates were issued over that same period, resulting in a 3.37% amendment rate. In this study, the cohort included both males and females with a ratio of 2:1. Their ages ranged from newborn to 103 years, with a mean age of 49 years. Of the 103 amended cases, amendments were made to the cause (n = 30) and often the manner (n = 21) of death listed on the death certificate; the remaining changes were limited to the autopsy report. The most common reasons for amendments included reception of delayed laboratory findings (35%), acquisition of additional medical history (22.5%), and typographic errors (15.5%). Typographic errors mainly occurred because of inaccuracies in the names originally provided to our office, the use of aliases by decedents, incorrect personal/demographic history, or various misspellings by funeral homes or medical examiner staff. The most significant reclassifications involved changing certified natural deaths to accidental overdoses and vice versa, based on toxicological analysis. Because of delays in specimen turnaround, these amendments often were made months after the original death certificate was issued. STAT urine drug screening has been helpful in reducing the number of amendments made, but certain drugs of significance are missed by rapid screens. Given that our office performed complete toxicological analysis on all cases over this period, it seems likely that we detected several overdoses that would have been missed if natural deaths were not routinely screened for potential toxins.
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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 | 0.054 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".