ALiEM Blog and Podcast Watch: Toxicology
Bibliographic record
Abstract
INTRODUCTION: The WestJEM Blog and Podcast Watch presents high-quality open-access educational blogs and podcasts in emergency medicine based on the ongoing Academic Life in Emergency Medicine (ALiEM) Approved Instructional Resources (AIR) and AIR-Professional (Pro) series. Both series critically appraise open-access educational blogs and podcasts in EM using an objective scoring instrument. This installment of the blog and podcast watch series curated and scored relevant posts in the specific topic of toxicology emergencies from the AIR-Pro Series. METHODS: The AIR-Pro Series is a continuously building curriculum covering a new subject area every two months. For each area, eight EM chief residents identify 3-5 advanced clinical questions. Using FOAMsearch.net and FOAMSearcher to search blogs and podcasts, relevant posts are scored by eight reviewers from the AIR-Pro editorial board, which is comprised of EM faculty and chief residents at various institutions across North America. The scoring instrument contains five measurement outcomes based on seven-point Likert scales: recency, accuracy, educational utility, evidence based, and references. The AIR-Pro label is awarded to posts with a score of ≥28 (out of 35) points. An "honorable mention" label is awarded if board members collectively felt that the blogs were valuable and the scores were > 25. RESULTS: A total of 31 blog posts and podcasts were included. Key educational pearls from the six high-quality AIR-Pro posts and four honorable mentions are summarized. CONCLUSION: The WestJEM ALiEM Blog and Podcast Watch series is based on the AIR and AIR-Pro Series, which attempts to identify high-quality educational content on open-access blogs and podcasts. This series provides an expert-based, crowdsourced approach towards critically appraising educational social media content for EM clinicians. This installment focuses on toxicology emergencies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.043 |
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".