Pharmacometrics: so much mathematics and why planes achieve their destinations with almost perfect results …
Bibliographic record
Abstract
Editorial: The mention of population analysis or pharmacokinetic and pharmacodynamic (PKPD) modelling often sends clinicians and clinical pharmacologists running, not wanting to know about mathematics and understanding complexity. Individualized dosing, optimal design of studies and mechanistic models of physiological processes are all ‘too hard’ often leading to another refrain – ‘and how do we apply this to our clinical practice?’ However, we are prepared to use complex technology and equipment in the practice of medicine that requires high level mathematical complexity, but we fear the use of similar methods to investigate, explore and understand drug therapy, drug effects and disease progression. Should we not be using the most modern methods to quantify and understand clinical pharmacology? Mathematics underlies much of how we function, specifically that we expect pilots and airlines to use the most advanced computers, simulations and analytic techniques to make flying safe. We often rely on global position system (GPS) devices to assist us in arriving at our destinations; require that our computer and smartphone operating systems provide fundamental conveniences and coordinate our schedules, phone calls and information and utilize Google™ to search for information on any number of topics. So why do we struggle with them in understanding drug effects?
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 | 0.050 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".