A systematic review of validated methods for identifying lymphoma using administrative data
Bibliographic record
Abstract
PURPOSE: To systematically review published studies for algorithms that identified lymphoma as a health outcome of interest in administrative or claims data and examined the validity of the algorithm to identify lymphoma cases. METHODS: A systematic literature search was executed using PubMed and the Iowa Drug Information Service database. Two investigators reviewed search results to identify studies using administrative or claims databases from the USA or Canada that both reported and validated an algorithm to identify lymphoma. RESULTS: The search identified 713 unique citations with 402 eliminated by an initial screen of the article abstract. The remaining 311 resulted in one study that identified and validated an algorithm. Ten other studies reported algorithms but were not validated. The validated study reported four possible algorithms that had a specificity (> 99%), but the algorithm using two diagnostic codes recorded within 2 months had the best positive predictive value (PPV = 62.83%) and a sensitivity (79.81%). The most comprehensive algorithm required multiple diagnostic codes 2 months apart or diagnostic, and procedure codes on the same day had the greatest sensitivity (88.31%) and a PPV = 56.69%. The algorithm that required only a single diagnostic or procedure code had the worst PPV (34.72%). CONCLUSION: The International Classification of Disease, Ninth Revision diagnostic, clinical procedure, and complication codes for lymphoma can identify incident hematologic malignancies and solid tumors with high specificity but with relatively low to moderate sensitivity and PPVs. When diagnostic and procedure codes were required on the same visit or multiple codes between visits, then PPV was increased. Relying on a single registry to confirm true positive cases is also not sufficient.
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 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 | 0.048 | 0.259 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".