“Waste not, want not”, or the cost of doing the wrong thing
Bibliographic record
Abstract
In 1995, Sinclair pointed out that it had taken an inordinately long time to understand that we had synthesized adequate evidence on antenatal corticosteroids (ANCS) to prevent respiratory distress syndrome (RDS) and its complications in preterms.1 Secondly, it then took even longer for the knowledge to be disseminated into practice.The dissemination problem was addressed by the NIH in a specific trial to enhance uptake of knowledge on ANCS by the obstetric community over 'standard' methods of teaching.2 In that cluster randomized trial, a package of teaching interventions aimed at the high-risk perinatal caregivers improved the uptake of ANCS in target populations of mothers at risk of preterm delivery by 108%.Yet it appears that despite these two seminal 'wake-up calls' to the community ---and despite the recommendations of key bodies such as ACOG 3,4 ---the omission of ANCS continues to plague perinatal---neonatal medicine.For example, between 2005 and 2007 in California, Lee found that ''of 15,343 eligible neonates, 23.1% did not receive antenatal steroids in 2005---2007.''5 Of these, a higher proportion of Hispanic mothers did not receive ANCS ---25.6%. 5 Disseminating this knowledge-based practice into poorly resourced or lower income countries has been even more challenging.6,7
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.063 | 0.052 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".