Printed In USA. A BRIEF ORIGINAL CONTRIBUTION A Note on the Grouping of Surveillance Data When Adjusting for Reporting Delays
Bibliographic record
Abstract
Analyses that adjust disease incidence data for reporting delays are often based on grouped data. A way of grouping data based on the quarter in which a case is reported, using all cases reported by a given cutoff date, is compared with the usual method of categorization based on a time-delay between diagnosis and reporting. The two methods of tabulation are illustrated using cases of the acquired immunodeficiency syndrome (AIDS) diagnosed and reported in Australia. A simple simulation study confirms that estimates of adjusted quarterly AIDS counts based on the quarter of report grouping are less variable than those based on the time-delay grouping. Am J Epidemiol 1997; 146:592-5. acquired immunodeficiency syndrome; epidemiologic methods; incidence reporting Surveillance systems that involve the compilation and analysis of case reports of disease at a central registry have become an important tool for the moni-toring of incidence trends in many countries. It has become widely recognized that there may be a con-siderable delay between the date of diagnosis and the reporting of a case to a central registry and that to
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".