The Drug Titration Paradox Updated and Reinterpreted: With Perfect Titration, Dose and Effect Will Be Uncorrelated
Bibliographic record
Abstract
The drug titration paradox is a clinical observation at a population level whereby patients receiving a high dose of medication appear to have a lower-than-average clinical effect, and patients receiving a low dose of medication appear to have a higher-than-average clinical effect. Consequently, there would be a negative correlation between dose and effect. The paradox was previously described as present in all titratable medications, particularly anesthetics. The underlying assumption of the paradox is that titration is gradual, that is, consisting of a sequence of relatively minor adjustments with the intent to avoid target over-shoot. This assumption may not hold true with technological advancements and automated drug delivery. The methodology of the paradox was re-explored without that assumption, both mathematically and with computer simulation of different control schemes that may be used to assist with drug titration. In these cases, achieving a positive correlation between dose and effect and reversing the paradox is possible. Recognizing that a perfectly titrated medication will have zero correlation coefficient at a population level, measuring the correlation coefficient over time can be a useful quality metric in evaluating automation technology in drug delivery.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".