Dead reckoning: can we trust estimates of mortality rates in clinical databases?☆
Bibliographic record
Abstract
OBJECTIVES: Databases almost invariably contain some errors and improvements to the quality of recorded data are costly. We sought to assess the extent to which given levels of error in a clinical database can lead to misleading mortality rates being derived. METHODS: We deliberately seeded a large database concerning congenital heart surgery involving over 17,600 operations, which we assumed to be error free, with errors at known rates of 0-20%. The effects of three different types of random error were explored: data omission, outcome miscoding (alive or dead) and the miscoding of procedures. For each error type, we compared the mortality rates calculated from the 'seeded' database to those calculated from the pristine database. RESULTS: Outcome miscoding typically results in overestimated mortality rates which for low-risk procedures may well give estimates over double the true value. Random data omission has relatively little effect. If procedure types are miscoded, procedure-specific mortality estimates for high-risk operations tend to be underestimates and those for low-risk operations overestimates. A mathematical model developed to examine these effects accurately forecasted the results of such error-seeding experiments. Software to implement this model is available free of charge on the Internet. CONCLUSION: Even small levels of data error can substantially affect the accuracy of mortality rate estimates, especially for low-risk operations. Such inaccuracy could lead to misleading analysis of institutional and individual surgeons' results. Our results suggest that caution is warranted in interpreting the mortality estimates derived from clinical databases. Our analysis goes beyond the realms of surgical mortality and concerns all adverse events whose frequency is rare.
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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 | 0.229 | 0.802 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".