Report on the 2003 Revision of the U.S. Standard Certificate of Death
Bibliographic record
Abstract
The National Center for Health Statistics (NCHS) is responsible for publishing Standard Certificates of Birth and Death for the United States of America. The standard certificates are revised roughly every 10 years. The revision process is designed to ensure that the standard certificates meet, as nearly as possible, the use for which they are intended at all levels: individual, local, state, and federal. The authors report on the most recent revision of the U.S. Standard Certificate of Death, recording the process and the role of the National Association of Medical Examiners in the process. Changes recommended during revision include requesting known aliases of a decedent and rearrangement of the certificate to provide more room for those items requesting dates and for describing how the injury occurred. New items have been added asking for information regarding traffic fatalities, the role of tobacco use in causing death, and whether female decedents were pregnant. Once approved by the Department of Health and Human Services, the new standard certificate will be made available to the states. Each state will have 2 years to adapt the U.S. Standard Certificate of Death to its use and to implement new state death certificates on January 1, 2003.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.030 |
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".