Systematic online academic resources (<scp>SOAR</scp>) review: Sickle cell disorders
Bibliographic record
Abstract
Background: Free open-access medical education (FOAM) resources have become highly utilized resources in emergency medicine education. However, FOAM content often lacks the traditional peer review process, leaving quality assessment to the readers. In this systematic online academic resource (SOAR) review, we apply a systematic methodology to assess the quality of FOAM resources on sickle cell disease (SCD). Methods: We searched keywords for SCD using FOAM Search and the top 50 FOAM websites listed on the Social Media Index. Resources found were screened using inclusion/exclusion criteria, and a total of 53 resources underwent full-text quality assessment using the revised Medical Education Translational Resources Impact and Quality (rMETRIQ) tool. Results: The search yielded 520 resources, of which 53 met the criteria for quality assessment. A total of eight posts (15.1% of posts) were identified as high quality (rMETRIQ ≥ 16). The most commonly addressed topics within SCD topics included acute chest syndrome, acute pain crisis and general review of SCD. A total of 11 posts (21% of posts) were found to have an rMETRIQ score of less than 7, which may indicate poor quality. The most commonly identified type of resource was personalized reading (64%) and a number of posts were deemed to not have an appropriate use due to poor quality (15%). Conclusions: We were able to systematically search a wide range of resources to identify, appraise, and organize FOAM resources on the topic of SCD. A final list of eight high-quality resources can guide trainees, educator recommendations, and FOAM authors.
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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 | 0.029 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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".