Probabilistic linkage in household survey on hospital care usage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the potential advantages and limitations of the use of the Brazilian hospital admission authorization forms database and the probabilistic record linkage methodology for the validation of reported utilization of hospital care services in household surveys. METHODS: A total of 2,288 households interviews were conducted in the county of Duque de Caxias, Brazil. Information on the occurrence of at least one hospital admission in the year preceding the interview was obtained from a total of 10,733 household members. The 130 records of household members who reported at least one hospital admission in a public hospital were linked to a hospital database with 801,587 records, using an automatic probabilistic approach combined with an extensive clerical review. RESULTS: Seventy-four (57%) of the 130 household members were identified in the hospital database. Yet only 60 subjects (46%) showed a record of hospitalization in the hospital database in the study period. Hospital admissions due to a surgery procedure were significantly more likely to have been identified in the hospital database. The low level of concordance seen in the study can be explained by the following factors: errors in the linkage process; a telescoping effect; and an incomplete record in the hospital database. CONCLUSIONS: The use of hospital administrative databases and probabilistic linkage methodology may represent a methodological alternative for the validation of reported utilization of health care services, but some strategies should be employed in order to minimize the problems related to the use of this methodology in non-ideal conditions. Ideally, a single identifier, such as a personal health insurance number, and the universal coverage of the database would be desirable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it