Item Response Theory, Computerized Adaptive Testing, and PROMIS: Assessment of Physical Function
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Patient-reported outcome (PRO) questionnaires record health information directly from research participants because observers may not accurately represent the patient perspective. Patient-reported Outcomes Measurement Information System (PROMIS) is a US National Institutes of Health cooperative group charged with bringing PRO to a new level of precision and standardization across diseases by item development and use of item response theory (IRT). METHODS: With IRT methods, improved items are calibrated on an underlying concept to form an item bank for a "domain" such as physical function (PF). The most informative items can be combined to construct efficient "instruments" such as 10-item or 20-item PF static forms. Each item is calibrated on the basis of the probability that a given person will respond at a given level, and the ability of the item to discriminate people from one another. Tailored forms may cover any desired level of the domain being measured. Computerized adaptive testing (CAT) selects the best items to sharpen the estimate of a person's functional ability, based on prior responses to earlier questions. PROMIS item banks have been improved with experience from several thousand items, and are calibrated on over 21,000 respondents. RESULTS: In areas tested to date, PROMIS PF instruments are superior or equal to Health Assessment Questionnaire and Medical Outcome Study Short Form-36 Survey legacy instruments in clarity, translatability, patient importance, reliability, and sensitivity to change. CONCLUSION: Precise measures, such as PROMIS, efficiently incorporate patient self-report of health into research, potentially reducing research cost by lowering sample size requirements. The advent of routine IRT applications has the potential to transform PRO measurement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it