Bibliographic record
Abstract
At no other time in the history of multiple sclerosis (MS) has the accurate measurement of health outcomes been so important. There are now many kinds of interventions of proven or potential efficacy available for people with MS and many other methods are under investigation. Not all outcomes that matter can be measured with a biological parameter. Many important outcomes of treatment can be assessed only by asking the patient directly. For clinical decision making, asking one good question, asking it consistently, and writing down the answer will produce historically accurate data to judge MS progression on life-altering constructs like fatigue, depression and pain. To get a total score from items in a questionnaire, Rasch Measurement Theory provides a way of estimating the extent to which the items form a linear continuum with mathematical properties. Preference-based measures, when the preferences are derived from patients, permit the impact of the multiple health dimensions associated with MS to be valued. The bottom line is, ask a good question and you will likely get a good answer, ask a poor question and assuredly, you will not.
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 | 0.049 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".