Minimum data cleaning recommendations for infection prevention and control acute care surveillance reporting: A solution for "garbage in, garbage out"
Bibliographic record
Abstract
Background: Outcome surveillance is an important component of infection prevention and control (IPAC) programs to guide healthcare decisions. It is crucial that the reported data are of the highest quality. Reviewing completeness, accuracy and timeliness of the data is important to reduce data inconsistencies. However, many IPAC staff do not have training in data cleaning or data quality activities. Methods: Expert epidemiologists across Canada have created best practice guidance for data quality activities to provide sufficient detail to improve this important patient safety activity. Most of these activities are simple checks to review the accuracy of the data without requiring additional review of the patient record or linkage to other datasets. Results: Based on consensus by surveillance experts across jurisdictions, comprehensive recommendations for data quality in IPAC surveillance programs were developed to improve completeness (22%), accuracy (68%), and timeliness (10%) of the data. Conclusion: The data quality activities list may be used in Canadian IPAC surveillance activities to support or improve existing surveillance data quality activities for IPAC programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.235 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".