Transient erythroblastopenia of childhood is an underdiagnosed and self‐limiting disease
Bibliographic record
Abstract
AIM: Transient erythroblastopenia of childhood (TEC) is an uncommon, benign normocytic anaemia of unknown cause, characterised by reduced or absent mature erythroid precursors in otherwise normocellular bone marrow and a complete spontaneous recovery. We present epidemiological data on paediatric TEC cases in a single centre over 30 years and compare them with published data. METHODS: In this retrospective study, epidemiological data on children diagnosed with TEC between 1978 and 2008 were collected and compared with published data. RESULTS: A total of 36 children (median age 19 months, 56% male children) were diagnosed. At presentation, median haemoglobin was 44 g/L with absolute reticulocyte count 0 × 10(9) /L; seventeen (47%) patients were neutropenic and 23 (64%) had platelet counts of more than 400 × 10(9) /L. The majority (78%) presented from 1983 to 1997, and 78% of articles reviewing 10 or more TEC patients were published between 1983 and 1992. CONCLUSION: Transient erythroblastopenia of childhood is now diagnosed less frequently in our institution than in the last two decades. Although the aetiology remains largely unknown, it is possible that changes in causative environmental factors contribute to making TEC a rare disease. Clinicians need to be aware of TEC in order to prevent unnecessary diagnostic and therapeutic measurements.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".