Chronogram: an R package for data curation and analysis of infection and vaccination cohort studies
Bibliographic record
Abstract
Motivation: Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated. Results: The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary. Availability and implementation: a user manual is available at: https://franciscrickinstitute.github.io/chronogram/.
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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.052 |
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".