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Additional file 1 of Comparison of dynamic updating strategies for clinical prediction models

2021· article· en· W6958536008 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsBrier scoreHeaderInterval (graph theory)CalibrationColumn (typography)StatisticQuarter (Canadian coin)Confidence interval

Abstract

fetched live from OpenAlex

Additional file 1: Table 1. Sample size, number of deaths, and mortality risk per quarter in the post-baseline period. Figure 1. Proportion of deaths versus time in the post-baseline period. Figure 2. Overall calibration for each update strategy when update interval equals 1 quarter and 100% new data are used. Triangles represent deciles of predicted probabilities; vertical lines at bottom of plot represent the distribution of subjects stratified by outcome status (below x-axis = alive, above x-axis = dead). Figure 3. Brier Score versus time when update interval equals 1 quarter and 100% new data are used. Figure 4. AUC vs. time when update interval equals 1 quarter and 100% new data are used. Figure 5. Hosmer-Lemeshow statistic vs. time when update interval equals 1 quarter and 100% new data are used. Figure 6. Calibration vs. time when update interval equals 1 quarter and 100% new data are used. A) calibration intercept (calibration-in-the-large); B) calibration slope. Table 2. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the update interval equals 1, 2, 4, or 8 quarters and 100% new data are used for each update. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Table 3. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the sliding window equals 1, 2, 4, or 8 quarters and the update interval equals 1 quarter. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Table 4. Brier Score (BS), AUC, Hosmer-Lemeshow statistic, calibration intercept and slope (from logistic calibration), and number of updates performed under each update strategy when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Table 5. Number of times each candidate update model was selected by the Closed Testing Procedure when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Table 6. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Figure 7. Boxplot of difference in Brier Score between each update strategy and never update calculated at each quarter versus sliding window length, when the update interval equals 4 quarters. Horizontal bars represent medians and interquartile ranges (IQR). Negative values reflect improvement compared never update. Figure 8. Overall calibration for each update strategy when update interval equals 4 quarters and 100% new data are used. Triangles represent deciles of predicted probabilities; vertical lines at bottom of plot represent the distribution of subjects stratified by outcome status (below x-axis = alive, above x-axis = dead). Figure 9. Brier Score versus time when update interval equals 4 quarters and 100% new data are used. Figure 10. AUC vs. time when update interval equals 4 quarters and 100% new data are used. Figure 11. Hosmer-Lemeshow statistic vs. time when update interval equals 4 quarters and 100% new data are used. Figure 12. Calibration vs. time when update interval equals 4 quarters and 100% new data are used. A) calibration intercept (calibration-in-the-large); B) calibration slope.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.114
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.8230.099

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.

Opus teacher head0.337
GPT teacher head0.451
Teacher spread0.114 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
DomainMethods
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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