The final part of the CRESS trilogy – how to evaluate the quality of stability studies
Bibliographic record
Abstract
High quality laboratory results are critical for patient management. However, poor sample quality can impact these results and patient safety. To ensure reliable and accurate results laboratories must be aware of each analyte's stability under various storage conditions and matrices to guarantee correct and dependable outcomes. This knowledge allows laboratories to define the allowable delay between sample collection and centrifugation/analysis for all analytes to guarantee appropriate results quality and interpretation. The EFLM Working Group for the Preanalytical Phase (WG-PRE) therefore established a 4-step plan to tackle this issue, aiming to standardize and harmonize stability studies for improved comparison and meta-analysis. The plan included the development of checklists and how-to guides for performing and reporting stability studies as well as a central resource of stability data. This manuscript deals with the issue of evaluating publications and incorporating them into a central resource. To evaluate stability studies, the CRESS checklist was used to structure 20 sections used to judge the quality of studies. Each section has 4 levels of quality, with scores converted to numerical values and weighted based on expert opinion. Based on this, a final score ranging from A to D was determined. The procedure was then tested on six manuscripts and checked for agreement between expert judgements. The results demonstrated that the proposed evaluation process is a useful tool to distinguish between best in class manuscripts and those of lower quality. The EFLM WG-PRE strongly believes that the provided recommendations and checklists will help improving stability studies both in quality and standardisation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".