Agreement between a health claims algorithm and parent-reported asthma in young children
Bibliographic record
Abstract
Introduction: Asthma prevalence is commonly measured in national surveys by questionnaire. The Ontario Asthma Surveillance Information System (OASIS) developed a validated health claims diagnosis algorithm to estimate asthma prevalence. The primary objective was to assess the agreement between two approaches of measuring asthma in young children. Secondary objectives were to identify concordant and discordant pairs, and to identify factors associated with disagreement. Study design and setting: A measurement study to evaluate the agreement between the OASIS algorithm and parent‐reported asthma (criterion standard). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Multivariable logistic regression was used to determine factors associated with disagreement. Results: Healthy children aged 1 to 5 years (n =3642) participating in the TARGet Kids! practice based research network 2008‐2013 in Toronto, Canada were included. Prevalence of asthma was 14% and 6% by the OASIS algorithm and parent‐reported asthma, respectively. The Kappa statistic was 0.43, sensitivity 81%, specificity 90%, PPV 34%, and NPV 99%. There were 3249 concordant and 393 discordant pairs. Statistically significant factors associated with asthma identified by OASIS but not parent report included: male sex, higher zBMI, and parent history of asthma. Males were less likely to have asthma identified by parent report but not OASIS. Conclusion: The OASIS algorithm identified more asthma cases in young children than parent‐reported asthma. The OASIS algorithm had high sensitivity, specificity, and NPV but low PPV relative to parent‐reported asthma. These findings need replication in other populations.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".