Accuracy and validity of using medical claims data to identify episodes of hospitalizations in patients with COPD
Bibliographic record
Abstract
PURPOSE: In Quebec, MED-ECHO database can be used to estimate inhospital length of stay (LOS) and number of hospitalizations (NOH) both accurately and reliably. However, access to MED-ECHO database is time-consuming. Quebec medical claims database (RAMQ) can be used as an alternative source to estimate these measures. Considering MED-ECHO as the 'gold standard,' this study examined the validity of using RAMQ medical claims to estimate LOS and NOH. METHODS: We used a cohort of 3768 elderly patients with chronic obstructive pulmonary disease (COPD) between 1990 and 1996 and identified those with inhospital claims. Inhospital LOS was defined as the total number of days with inhospital claims. Various grace periods (1-15 days) between consecutive claims were considered for the estimation of LOS and NOH. RAMQ and MED-ECHO databases were linked using unique patient identifiers. Estimates obtained from RAMQ data were compared to those from MED-ECHO using various measures of central tendency and predictive error estimates. RESULTS: Overall, 32.7% of patients were hospitalized at least once during the study period based on RAMQ claims, as compared to 32.0% in MED-ECHO ( p-value = 0.51). The best estimates [mean (p-value)] were found to be those obtained when using a 7-day grace period. RAMQ versus MED-ECHO estimates were: 12.2 versus 13.5 days (< 0.001) for LOS and 3.6 versus 3.7 times (0.36) for NOH. CONCLUSIONS: RAMQ medical claims can be used as a reliable source to estimate LOS and NOH, particularly when time and resources are restricted. RAMQ, however, should be used with caution since slight underestimations may occur.
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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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".