Neurological Registry Quality Control and Quality Assurance
Bibliographic record
Abstract
This section of the guideline discusses procedures and best practices around quality control and quality assurance.In developing this section of the guideline we reviewed available literature and best practice; consulted with registry and disease experts; and derived consensus recommendations.Quality, as defined by the International Standards Organization (ISO) in standard ISO 8402:1994, is the "totality of characteristics of an entity that bear on its ability to satisfy stated and implied needs."194 In the context of registries, this means that registry data characteristics must altogether satisfy the intended and implied needs of the registry purpose.For example, if the purpose of your registry is to study all female adults of child-bearing age with epilepsy; then your registry data must consist only of female adults of child-bearing age who have a diagnosis of epilepsy.It is important to note that quality and registry purpose are inherently related.Registry creators will therefore need to define what quality means for their specific purpose(s).While quality control and quality assurance are related concepts it is important to understand that they are different.Quality assurance (QA) is the process that maintains a desired level of quality.195 QA is a proactive process done in advance of obtaining an outcome.Examples of QA activities might include audits, training, procedure documentation, selection of quality tools etc. Quality control (QC) is the assessment of whether an outcome meets quality expectations.195 QC is a reactive process done once an outcome has been obtained.Examples of QC activities might include testing a product sample to determine if it meets requirements; or conducting a site inspection visit.Useful registries must have good quality data.
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 | 0.086 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".