Insulin Sensitivity and Its Measurement: Structural Commonalities among the Methods<sup>1</sup>
Bibliographic record
Abstract
Insulin is the principal hormone of metabolic regulation. Reduced responses to insulin constitute an underlying feature of type 2 diabetes. It is, therefore, incumbent on those who work in this area (as well as many others) to characterize this response, in as simple and consistent a way as possible, so that this measure can be used both in the investigational and clinical setting. This type of approach, although eminently useful, is necessarily an oversimplification. Not only does insulin sensitivity change in pathological situations, but also in normal physiology. Tissue-specific, metabolite-specific, as well as process-specific responses may be expected to occur. Variations also occur in time-depending on the physiological state of the individual (e.g. pregnancy, aging) or following diurnal rhythms. It is perhaps remarkable that any consistent assessment of overall insulin sensitivity can be made. The observation that this can often be achieved has led to hypotheses suggesting that sensitivity to insulin is primarily determined at a single site (tissue, metabolite). At the same time, there are many discussions about the inconsistencies inherent in different approaches to the measurement of this parameter, suggesting that some of these variants, metabolic or otherwise, could lead to the low correlation between methods sometimes seen. Nevertheless, most methods used in the assessment of insulin sensitivity examine the response to insulin of a single metabolite, glucose, primarily in the muscle and liver, and under fasting conditions and should, therefore, demonstrate insulin sensitivity that is comparable among methods.
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 | 0.042 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".