Moving towards harmonized reporting of serum and urine protein electrophoresis
Bibliographic record
Abstract
During the last decade, surveys by questionnaire in Canada, Australia and New Zealand revealed wide variation in reporting practices by laboratories and individual practitioners in the interpretation of serum and urine protein electrophoresis (PE). Such variation has potential to adversely impact patient outcomes if report structure is inconsistent or if the messaging is incorrectly perceived by the receiving physician. Concerted efforts have been initiated to promote harmonization in the use of interpretative comments. The primary goal is to add value through clear communication with requesting physicians in the interest of quality patient care. Resistance to a harmonized approach largely reflects longstanding personal reporting habits and preferences but change can be more readily embraced if the new system is intuitive, easy to use and saves time in reporting.
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 | 0.060 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.028 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".