Case Definitions for Chronic Rhinosinusitis in Administrative Data: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The use of administrative data for pharmacoepidemiologic research on chronic rhinosinusitis (CRS) has become increasingly popular. Although large sample sizes and ease of accessibility have made electronic health data an attractive data option, the risk of inaccurate cohort identification can lead to biased outcomes. OBJECTIVES: The objectives of this systematic review were to (1) report current case definitions for CRS used in administrative data base research, and (2) define the various administrative data bases used for CRS research. METHODS: Medical literature data bases were searched from the date of their inception to February 1, 2015. Included studies were publications that obtained CRS-specific data from a health records data base. Studies were excluded if they evaluated a non-CRS cohort, failed to use or report an international classification of disease (ICD) code in the case definition, or published in a non-peer-reviewed journal. RESULTS: Of the 27 studies that met inclusion criteria, 8 different CRS case definitions were identified and 13 administrative data bases were evaluated. Of the 8 different CRS case definitions identified, only one was validated. The most commonly used CRS case definition was the ICD-9 473.x code alone. CONCLUSION: To optimize the accuracy of pharmacoepidemiologic research for CRS that used administrative data, it is important to apply appropriate case definitions for CRS. Various nonvalidated CRS case definitions are currently being used in administrative data base research. There is a need to develop a generalizable and validated ICD-based CRS case definition to increase the accuracy of future pharmacoepidemiologic research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".