The Current State of Validation of Administrative Healthcare Databases in Italy: A Systematic Review
Bibliographic record
Abstract
Background: Administrative healthcare databases are widely present in Italy. Our aim was to describe the current state of healthcare databases validity in terms of discharge diagnoses (according to the International Classification of Diseases, ICD-9 code) and their output in terms of research. Methods: A systematic search of electronic databases including Medline and Embase (1995-2013) and of local sources was performed. Inclusion criteria were: healthcare databases in any Italian territory routinely and passively collecting data; medical investigations or procedures at patient level data; the use of a validation process. The quality of studies was evaluated using the STARD criteria. Citations of the included studies were explored using Scopus and Google Scholar. Results: The search strategy allowed the identification of 16 studies of which 3 were in Italian. Thirteen studies used regional administrative databases from Lombardia, Piemonte, Lazio, Friuli-Venezia Giulia and Veneto. The ICD-9 codes of the following diseases were successfully validated: amyotrophic lateral sclerosis (3 studies in four different regional administrative databases), stroke (3 studies), gastrointestinal bleeding (1 study), thrombocytopenia (1 study), epilepsy (1 study), infection (1 study), chronic obstructive pulmonary disease (1 study), Guillain-Barre syndrome (1 study), and cancer diseases (4 studies). The quality of reporting was variable among the studies. Only 6 administrative databases produced further research related to the validated ICD-9 codes. Conclusion: Administrative healthcare databases in Italy need an extensive process of validation for multiple diagnostic codes to perform high quality epidemiological and health services research.
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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 | 0.144 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.028 | 0.034 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".