Use FlowRepository to share your clinical data upon study publication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A fundamental tenet of scientific research is that published results including underlying data should be open to independent validation and refutation. Data sharing encourages collaboration, facilitates quality and reduces redundancy in data production. Authors submitting manuscripts to several journals have already adopted the habit of sharing their underlying flow cytometry data by deposition to FlowRepository-a data repository that is jointly supported by the International Society for Advancement of Cytometry, the International Clinical Cytometry Society and the European Society for Clinical Cell Analysis. De-identification is required for publishing data from clinical studies and we discuss ways to satisfy data sharing requirements and patient privacy requirements simultaneously. Scientific communities in the fields of microarray, proteomics, and sequencing have been benefiting from reuse and re-exploration of data in public repositories for over decade. We believe it is time that clinicians follow suit and that de-identified clinical data also become routinely available along with published cytometry-based findings. © 2016 International Clinical Cytometry Society.
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.
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 | 0.004 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it