Bibliographic record
Abstract
In the current era of precision medicine, the existing nephropathology paradigm of light microscopy, immunofluorescence, and electron microscopy will become increasingly insufficient. There will be an expectation to supplement these traditional diagnostic tools with patient-specific information related to a growing understanding of molecular pathophysiology. Next generation sequencing technologies are expected to play a key role in the future of nephropathology, but transcriptomics is poised to represent the first major foray into routine molecular testing. The introduction of molecular techniques into clinical nephropathology has been hindered in part by the reliance of existing platforms on fresh tissue samples. The NanoString gene expression system works with formalin-fixed paraffin-embedded tissue and thus represents a promising solution to this technical barrier that may finally allow for the translation of recent transcriptomics discoveries into the enhancement of patient care. Widespread adoption of this new diagnostic dimension will require ongoing multidisciplinary cooperation between pathologists and clinicians, including molecular testing consensus generation and rigorous multicenter validation.
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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".