Using Reproducible Data Visualizations to Augment Decision-Making During Suppression of Small Counts
Bibliographic record
Abstract
IntroductionWhen reporting disease rates to the public, a health system must take precaution to protect released data from re-identification risks. While specific guidelines and methods vary across data systems and governances 1 , redaction of cells with small values is a key component in any approach for preparing data for public release. These preparations, when conducted manually, have proven to be arduous, time consuming, and prone to human error. Although finding a “small” value (e.g. “< 5 ” ) is straightforward, detecting conditions in which suppressed values could be recalculated from related cells involves human judgement. Objectives and ApproachGuided by the real-world objective to reports the rates of chronic diseases in British Columbia, we aimed to design a reproducible workflow that would augment human decision-making and offer a nimble quality control tool, approachable by epidemiologists without technical background. Our workflow (1) splits data into disease-by-year data frames of a specific form, (2) applies a sequence of algorithms trained to recognize conditions that made recalculation of suppressed values possible and (3) prints a graph for each case of suggested automatic redaction to be confirmed by a human. ResultsThe augmented suppression system was successfully integrated into the maintenance of Chronic Disease Dashboard, an online reporting tool of the Observatory for Population and Public Health designed to address the gap in surveillance of chronic diseases in British Columbia. Anticipating the evolution of suppression logic, we isolated the logical tests responsible for redaction and provided several options to vary the degree of preserved information. Conclusion / ImplicationsInstead of employing a complex generalizable solution, we make a case for organizing the procedure for small cell redaction as a data visualization task, allowing for straightforward quality control of suppression decision and thus more approachable to a non-technical audience, as well as for employing such learning devices as workflow maps and function dependency trees for structuring applied projects and ensuring their reproducibility.
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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 | 0.027 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".