Registries in pediatric anesthesiology: A brief history and a new way forward
Bibliographic record
Abstract
Clinical registries are multicenter prospective observational datasets that have been used to examine current perioperative practices in pediatric anesthesia. These datasets have proven useful in quantifying the incidence of rare adverse outcomes. Data from registries can highlight associations between severe patient safety events and patient and procedure-related risk factors. Registries are an effective tool to delineate practices and outcomes in niche patient populations. They have been used to quantify uncommon complications of medications and procedures. Registries can be used to generate knowledge and to support quality improvement. Multicenter engagement can promote best clinical practices and foster professional networks. Registries are limited by their observational nature, which entails a lack of randomization as well as selection and treatment bias. The maintenance of registries over time can be challenging due to difficulties in modifying the included variables, collaborator fatigue, and continued outlay of resources to maintain the database and onboard new sites. These latter issues can lead to decreased data quality. In this article, we discuss key insights from several pediatric anesthesia registries and propose a new type of registry that addresses some shortcomings of the current paradigm.
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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.011 | 0.040 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".