The validity of algorithms using health administrative data in identifying cancer-related events in adolescents and young adults: A population-based study using the IMPACT cohort.
Bibliographic record
Abstract
e18168 Background: Despite the importance of estimating population level cancer outcomes, most registries do not collect critical events such as relapse and progression. Attempts to use health administrative data to identify these events have focused on older adults and have been mostly unsuccessful. We developed and tested administrative data-based algorithms in a population-based cohort of adolescents and young adults (AYA) with cancer. Methods: We identified all Ontario AYA 15-21 years of age diagnosed with leukemia, lymphoma, sarcoma, or testicular cancer between 1992 and 2012. Chart abstraction was used to determine the end of initial treatment (EOIT) date and subsequent cancer-related events (progression, relapse, second cancer). Linkage to population-based administrative databases identified fee and procedure codes indicating cancer treatment or palliative care. Algorithms that determined EOIT based a time interval free of treatment-associated codes, and new cancer-related events based on billing codes, were compared to chart abstracted data. Results: The cohort comprised 1,404 patients. Time periods free of treatment-associated codes did not validly identify EOIT dates; using subsequent codes to identify new cancer events was thus associated with low sensitivity (56.2%). However, using administrative data codes that occurred after the EOIT date based on chart abstraction, the first cancer-related event was identified with excellent validity (sensitivity 87.0%, specificity 93.3%, PPV 81.5%, negative predictive value 95.5%). Conclusions: While administrative data alone did not validly identify cancer-related events, using administrative data in combination with chart collected EOIT dates was associated with excellent validity. The collection of EOIT dates by cancer registries would significantly expand the potential of administrative data linkage to assess cancer outcomes.
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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 | 0.043 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".