Bibliographic record
Abstract
BACKGROUND: In de-identified data, exact birth dates are suppressed to maintain the confidentiality of research participants. When the partial birth date includes only the month and year, researchers who need exact dates must impute a day of the month. In some deidentified datasets, the day of the week is also provided, but this variable is uncommonly incorporated into the imputation of partial dates. OBJECTIVE: To examine the extent to which misclassification is reduced by incorporating the day of the week into partial birth date imputation. METHODS: We simulated a population of 594,677 people using the distribution of birthdays in England and Wales in 2024. We imputed birth dates using four methods: (1) the first day of the month, (2) the 15th of the month, (3) randomly selecting a day of the month and (4) randomly selecting a day of the month conditional on the day of the week. We quantified misclassification as the median number of days between the imputed and true birth date and as the cumulative percentage of the population whose imputed birth date fell within a given number of weeks of their true birth date. RESULTS: Incorporating the day of the week reduced misclassification, with a median of 7 days between the imputed and exact birth dates compared to 8-15 for the other methods. For nearly a quarter of the population, their imputed birth date was their true birth date, compared to 3% in other methods. However, using the 15th day of the month was the best method to ensure that no misclassification was greater than 3 weeks. CONCLUSION: Incorporating day of the week into random birth date selection reduced misclassification. This method is easily accomplished in standard statistical software.
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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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".