Death Certification in Northern Alberta
Bibliographic record
Abstract
Errors in death certification can directly affect the decedent's survivors and the public register. We assessed the effectiveness of an educational seminar targeting frequent and important errors identified by local death certificate (DC) evaluation. Retrospective review of 1500 DCs categorized errors and physician specialty. A 60-minute didactic/case-based seminar was subsequently designed for family medicine physician (FAM) participants, with administration of presurvey, immediate post, and 2-month postsurveys. Most DCs were completed by FAM (73%), followed by internists (18%) and surgeons (3%). Error occurrence (EO) rate ranged between 32 and 75% across all specialities. Family medicine physician experienced in palliative care had the lowest EO rate (32%), significantly lower (P < 0.001) than FAM without interest in palliative care (62%), internal medicine (62%), and surgery (75%). Common errors were use of abbreviations (26%), mechanism as underlying cause of death (23%), and no underlying cause of death recorded (22%). Presurvey participants (n = 72) had an overall EO rate of 72% (64% excluding formatting errors). Immediate postsurvey (n = 75) and 2-month postsurvey (n = 24) participants demonstrated significantly lower overall EO (34% and 24%, respectively), compared with the Pre-S (P < 0.05). A 60-minute seminar on death certification reduced EO rate with perceived long-term effects.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".