Bibliographic record
Abstract
I am prompted by the discussion in the October issue of the]ournal to comment about the difficulty of writ-ing guidelines based on generic treatment of hyper-tension that do not take into account the heterogene-ity of the hypertensive population. I commented pre-viously ’ on a similar difficulty encountered by the Canadian Consensus Conference Guidelines a few years ago. It is particularly important to identify the cause of hypertension in patients whose hypertension is se-vere and resistant. As I have previously reported,’ 5 % to 10 % will be adrenocortical (mostly due to hy-perplasia). Approximately 10 % to 20 % of patients with severe hypertension will be renovascular. Among the high renin cases, other important diag-noses that should not be missed will turn up, includ-ing 1.75 % to 3.5 % of cases with hypernephroma.’ Most of our patients with adrenocortical hyperten-sion can be managed medically once the diagnosis is made; indeed, we have only had to do surgery in 6% of cases. However, knowing the diagnosis is the key to successful management; such patients often re-quire large doses of potassium/magnesium-sparing diuretics, such as spironolactone or amiloride, whereas they often cannot tolerate even low doses of thiazides if they are not “covered ” by ion-sparing di-
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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 | 0.051 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.098 | 0.007 |
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".