Bibliographic record
Abstract
Sir, I read with interest the paper by Chow etal.1 (‘Long-term follow-up of patients with asymptomatic isolated microscopic haematuria’). The title of the paper is somewhat misleading, as in pure isolated microscopic haematuria, the urine protein excretion rate is <100 mg /day (<0.1 g /day),2 but in this study the authors also included patients with microscopic haematuria and minimal proteinuria (>0.2 g /day) and, as expected (as shown in Table 1 and Figure 2), the majority of the patients in the latter group had adverse events, because of the higher baseline protein excretion rate, possibly suggesting an underlying nephrological process. This paper re-iterates the well known fact that patients with minimal proteinuria are increased risk for disease progression.2 We shouldn't mix apples with oranges, and then conclude that a small amount of oranges would give apples a sour taste. The title ‘Long-term follow-up of patients with microscopic haematuria, with and without minimal proteinuria,’ might have been more appropriate for this paper, which highlights the significance of ‘even minimal proteinuria’ and the need for long-term follow-up of such patients.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".