Additional file 1 of Revisiting the epidemiology of pertussis in Canada, 1924–2015: a literature review, evidence synthesis, and modeling study
Bibliographic record
Abstract
Additional file 1: Wu-IRC-pertussis-supp-BMCPH-June10.pdf. “Supplementary Material”. Supplementary material, comprising sections A1, A2 and A3. Table S1. References to data sources accessed/consulted for different sub-periods of 1952–2015 for age stratified and 1924–2015 for total reported incidences. Table S1. References to data sources accessed/consulted for different sub-periods of 1952–2015 for age stratified and 1924–2015 for total reported incidences. Table S1. References to data sources accessed/consulted for different sub-periods of 1952–2015 for age stratified and 1924–2015 for total reported incidences. Fig. S1. Yearly incidence rates for total R (blue) and age-unknown R ̂ u $$ {\hat{R}}^u $$ (green) reported cases of pertussis since the start of national age-stratified reporting in 1952. Fig. S2. Comparison of age-stratified distributions calculated in three different ways: 1) age distribution that assuming all cases have the same age distribution as those ageknown cases (R1); 2) age distribution estimated by the bootstrapping method (R2); 3) age distribution of those age-known cases only (R3). An application of the Kruskal-Wallis test shows no statistically significant difference among these distributions. Fig. S1. Yearly adjusted age-stratified incidence rates for combined age-supplied reported cases calculated via the bootstrapping method. Fig. S4 The age-stratified proportions (with respect to total) for each of the four provinces Ontario, Quebec, British Columbia, and Alberta in years of 1991, 1992, 1993, and 1994.
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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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.644 | 0.026 |
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".