Bibliographic record
Abstract
There are several measures of disease frequency that are used in the epidemiological and medical literature. A perfect diagnostic test would always identify patients as positive if they have the disease and would always be negative in patients without a disease. Descriptive statistics are used to summarise and describe distributions of data. While traditional methods use a test and/or a diagnostic tool to predict a disease state and/or outcome, as discussed at the beginning of this chapter, predictive analysis is becoming increasingly popular due to the emergence of sophisticated data science. When performing research, consulting with a biostatistician is paramount to ensure that the study is designed in a way that facilitates proper analysis. However, having knowledge of the concepts in biostatistics ensures that the clinical goals of the study meet the study design and the analytical plan.
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 | 0.045 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.026 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".