Reliable assessment of the incidence of childhood autoimmune hemolytic anemia
Bibliographic record
Abstract
BACKGROUND: Childhood autoimmune hemolytic anemia (AIHA) is a rare and severe disease characterized by hemolysis and positive direct antiglobulin test (DAT). Few epidemiologic indicators are available for the pediatric population. The objective of our study was to reliably estimate the number of AIHA cases in the French Aquitaine region and the incidence of AIHA in patients under 18 years old. PROCEDURE: In this retrospective study, the capture-recapture method and log-linear model were used for the period 2000-2008 in the Aquitaine region from the following three data sources for the diagnosis of AIHA: the OBS'CEREVANCE database cohort, positive DAT collected from the regional blood bank database, and the French medico-economic information system. RESULTS: A list of 281 different patients was obtained after cross-matching the three databases; 44 AIHA cases were identified in the period 2000-2008; and the total number of cases was estimated to be 48 (95% confidence interval [CI]: 45-55). The calculated incidence of the disease was 0.81/100,000 children under 18 years old per year (95% CI 0.76-0.92). CONCLUSION: Accurate methods are required for estimating the incidence of AIHA in children. Capture-recapture analysis corrects underreporting and provides optimal completeness. This study highlights a possible under diagnosis of this potentially severe disease in various pediatric settings. AIHA incidence may now be compared with the incidences of other hematological diseases and used for clinical or research purposes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".