Bibliographic record
Abstract
BACKGROUND: The term population is frequently used in clinical research and statistics, but concepts are multiple and confusing. Populations are a roundabout way of conceiving classifications, generalizations and inductive inferences. When misapplied, the term can lead to serious errors in study design, analysis and interpretation. METHODS: We review various notions of populations, their relationship with statistical inferences, and whether they refer to persons, variables or theoretical constructions. RESULTS: There are design- and model-based statistical inferences. The simplest design-based inference is from a representative random sample to a real definite population, but it is rarely possible or even pertinent in clinical research. The term population rarely concerns patients. Super-populations are theoretical postulates of statistical models that attempt to explain the distributions and relationships of variables. Pseudo-populations are mathematical constructs used to balance baseline characteristics to extract causal inferences from observational studies. Statistical populations are as numerous as variables. This leads to an explosion of entities, with much room for divergent analyses and manipulations. Target populations are to whom study results should apply. In the absence of a real population, they are erroneously assimilated to the eligibility criteria of study subjects. The inductive problem remains unsolved, for inferences from study subjects to future patients then depend on the meaning of words used in indefinite descriptions. CONCLUSION: The term population often hides more than it reveals regarding problems of generalizations and inferences. Because the term leads to errors and misconceptions, it should rarely be used in clinical research.
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 | 0.322 | 0.539 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.003 | 0.043 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".