Validity and usefulness of the RAE-CMBD studying patients hospitalised with influenza
Bibliographic record
Abstract
OBJECTIVE: Understanding the hospital impact of influenza requires enriching epidemiological surveillance registries with other sources of information. The aim of this study was to determine the validity of the Hospital Care Activity Record - Minimum Basic Data Set (RAE-CMBD) in the analysis of the outcomes of patients hospitalised with this infection. METHODS: Observational and retrospective study of adults admitted with influenza in a tertiary hospital during the 2017/2018 and 2018/2019 seasons. We calculated the concordance of the RAE-CMBD with the influenza epidemiological surveillance registry (gold standard), as well as the main parameters of internal and external validity. Logistic regression models were used for risk adjustment of in-hospital mortality and length of stay. RESULTS: A total of 907 (97.74%) unique matches were achieved, with high inter-observer agreement (ƙ=0.828). The RAE-CMBD showed a 79.87% sensitivity, 99.72% specificity, 86.71% positive predictive value and 99.54% negative predictive value. The risk-adjusted mortality ratio of patients with influenza was lower than that of patients without influenza: 0.667 (0.53-0.82) vs. 1.008 (0.98-1.04) and the risk-adjusted length of stay ratio was higher: 1.15 (1.12-1.18) vs. 1.00 (0.996-1.001). CONCLUSIONS: The RAE-CMBD is a valid source of information for the study of the impact of influenza on hospital care. The lower risk-adjusted mortality of patients admitted with influenza compared to other inpatients seems to point to the effectiveness of the main clinical and organisational measures adopted.
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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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".