Bibliographic record
Abstract
The reformatting and changes that were made to the Asthma Control Questionnaire (ACQ) 1 for the Post-cold Asthma Control and Exacerbation (PAX) study 2 raise some important concerns about modifying validated questionnaires. Just like mechanical and electrical measurement instruments, validated questionnaires are precision measurement instruments. The only difference being that they measure subjective rather than objective health status. Like any mechanical or electrical instrument, a great deal of care and expertise goes into the development of these questionnaires. Many studies have provided developers with the knowledge of how to: specify what the questionnaire is intended to measure (its construct); structure and select the right questions; formulate the responses; select the time specifications; optimise the page formulation for accurate completion; conduct validation studies (measurement properties and whether the instrument is measuring what it is meant to measure) and provide users the wherewithal to place a clinical interpretation on the data. For the same reason that one would never think of changing the numbers on the dial of a mechanical spirometer, one should never change a validated questionnaire. Even very small changes can destroy its validity. Questions are selected by well-established methods (usually either “importance” or “factor analysis”) 3 and their position in the questionnaire carefully ordered. Wording is checked for ease and accuracy of understanding (cognitive debriefing). For the analysis, each question has a weighting. For some questionnaires, this means that an algorithm must be used ( e.g. The Short Form (SF)-36 Health Survey) 4, in others ( e.g. the ACQ) questions are selected in such a way that they have equal weighting and the overall score is the mean of all the responses. The wording of questions should never be changed. Shortened versions should only be used when they have been …
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".