Challenges and opportunities in establishing an Health Examination Survey
Bibliographic record
Abstract
In Italy, the last 30 years witnessed the implementation of cross-sectional surveys providing baseline data on numerous risk factors collected from random samples of the adult general population. In order to support those groups who would like to implement an health examination survey (HES), according to the experience of the CUORE Project surveys, the objective of this paper is to describe some information related to the organization of a survey (examination sites and sampling, selection of analytic laboratory, coordination and personnel involved, sample selection, recruitment and appointment scheduling, informative notice and informed consent, participation rate, non-participation bias, quality assurance, survey data, long term storage of the samples, internal quality control, external quality assessment, feedback to participants, error checking, correction and documentation of the data, transfer and storage of the data, statistical analyses and interpretation of results, dissemination of results), usually shortly described in scientific papers but relevant when an HES is planned.
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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 | 0.463 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".