Biological aspects of neuroblastomas identified by mass screening in quebec
Bibliographic record
Abstract
BACKGROUND: Neuroblastoma has several characteristics that suggest that preclinical diagnosis might improve outcome. Therefore, the Quebec Neuroblastoma Screening Project was undertaken from 1989 to 1994 to examine infants at 3 weeks and 6 months by measuring urinary catecholamine metabolites. PROCEDURE: Over the 5-yr period, 45 tumors were detected by screening, 20 were identified clinically prior to the third week, and 64 were identified clinically at a later time. We analyzed available tumors for Shimada histopathology, tumor ploidy, MYCN copy number and serum ferritin. RESULTS: Of the tumors detected by screening, only 2 of 45 tested had unfavorable histology, 2 of 45 had diploid or tetraploid DNA content, 0 of 43 had MYCN amplification, and 4 of 44 had elevated serum ferritin. All of these patients are alive and well. The 20 patients detected prior to the 3-week screen had similar biological characteristics. In contrast, of the patients detected clinically after 3 weeks of age, 19 of 51 testedhad unfavorable histology, 25 of 66 had diploid or tetraploid tumors, 12 of 56 had MYCN amplification, and 14 of 54 had elevated ferritin. CONCLUSIONS: The difference between the screened and clinically detected cases was highly significant for each biological variable. Preliminary data on other biological variables, such as neurotrophin expression and allelic loss on 1 p in these patients are consistent with the above findings. These data suggest that mass screening for neuroblastoma at or before 6 months of age detects almost exclusively tumors that have favorable biological characteristics, many of which might have regressed spontaneously. Thus, continued mass screening for neuroblastoma at 6 months is unlikely to accomplish its intended goal, and should probably be discontinued.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".