Bibliographic record
Abstract
Re: RK Whyte, AL Jefferies; Canadian Paediatric Society, Fetus and Newborn Committee. Red blood cell transfusion in newborn infants. Paediatr Child Health 2014;19(4):213–222. Many thanks to Drs Whyte and Jefferies for their excellent review of neonatal red blood cell transfusions, published in the April 2014 issue of the Journal, and their willingness to address this controversial topic. However, I would like to raise concerns regarding their recommended thresholds for transfusion for anemia of prematurity. The neurodevelopmental outcomes of the Premature Infants in Need of Transfusion (PINT) study, published in 2009 (1), clearly indicate (albeit in the authors’ post hoc analysis) a benefit of higher transfusion thresholds in reducing the rate of mild cognitive delay (motor development index [MDI] <85). In the absence of contradictory evidence, this critically important observation cannot be ignored. This year’s updated Canadian Paediatric Society Position Statement recommendation on this matter states that “it would be prudent to maintain hemoglobin levels above the thresholds described in Table 1”, which references the lower transfusion thresholds from the PINT study. In fact, what little evidence has been published on long-term neurodevelopmental outcomes supports the higher transfusion cut-off values. In light of this, the Position Statement should, at the very least, support individual centres’/clinicians’ choice to follow either set of thresholds. I have a feeling that many neonatologists around Canada share the same concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.039 | 0.023 |
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".