Bibliographic record
Abstract
This article was submitted by Janielee Williamson, RN, Cochrane, Alberta. Janie is the project coordinator for a multi-centre research project across Alberta looking at the best method for disseminating practice guidelines to physicians. The project personnel are working in collaboration with the Alberta Medical Association CPG Committee to determine which method improves the delivery of best practice for children with croup, while ensuring optimum care for the child and determining which method has the best overall benefit for the health care system and the family. All emergency department personnel are familiar with this scenario: 18-month-old boy presents to the emergency department at 2 a.m. on December 18. Parents look worried, yet they are baffled. As they tell their story, they are almost apologetic, “Really, he was much worse at home, he seems so much better since we drove to the hospital. He woke up in distress, he couldn’t breathe and he was making this awful noise when he took a breath in. And his cough – I’ve never heard anything like it – he sounded like a dog... or no, more like a seal. Really, it was terrible!” There, in mom’s arms, is a happy, quiet boy looking around. When you try to examine him, his cry is stridorous and you hear the bark... reassuringly you smile back at the mom. “Yes, we know, and no we don’t have magic doors. HE HAS CROUP.”
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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".