Attitudes toward the autopsy--an 8-state survey.
Bibliographic record
Abstract
CONTEXT: National autopsy rates have declined for several decades, and the reasons for such decline remain contentious. OBJECTIVE: To elicit the opinions of one group of crucial decision makers as to the reasons for this decline and possible modes of reversal. DESIGN: A 2-part survey, composed of multiple choice questions and questions requesting specific data on autopsy rates and costs. SETTING: Illinois, Iowa, Louisiana, Minnesota, Nebraska, North Dakota, South Dakota, and Wisconsin. PARTICIPANTS: Hospital administrators within the 8 states. MAIN OUTCOME MEASURES: Six-point survey scale relating to reasons for autopsy decline and possible remedial measures, as well as estimates of autopsy rates and costs. RESULTS: The response rate was 43% and the median autopsy rate was 2.4% (mean 6.1%). The median cost of autopsy was estimated at $852 (mean $1275). Larger hospitals were associated with higher autopsy rates than smaller hospitals (9.6% vs 4.0%), and teaching hospitals had a significantly higher autopsy rate than nonteaching institutions (11.4% vs 3.8%). Autopsy rates also varied by type of hospital control, with federal government hospitals having the highest autopsy rate at 15.1%. Sixty-six percent of all respondents agreed that current autopsy rates were adequate. Of the respondents, the highest percent (86%) agreed that improved diagnostics contributed to the decline in autopsies, and the highest percent (78%) agreed that direct payment to pathologists for autopsies under the physician fee schedule might lead to an increase in autopsies. CONCLUSIONS: Our data support the conclusion that the decline in autopsy performance is multifactorial, although the variable that dominates in this analysis is the contentious perception that improved diagnostic technology renders the autopsy redundant. The rate of autopsy is conditional, at least in part, on individual hospital characteristics such as large hospital size, teaching status, and federal ownership. Three underlying factors may explain these associations: resources, mission, and case mix. An important factor in declining autopsy rates appears to be the changing economic landscape, with its increased focus on cost control within both the public and private healthcare sectors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".