Identifying cut‐off scores with neural networks for interpretation of the incontinence impact questionnaire
Bibliographic record
Abstract
We propose to determine cut-off scores for the Incontinence Impact Questionnaire (IIQ) based on the neural network (NN) approach. These cut-off scores should discriminate between patients having poor, moderate, or good quality of life (QoL) secondary to their incontinence problems. Data from two prospectively completed QoL questionnaires, the IIQ (n = 237) and the MOS 36-Item Short-Form Health Survey (SF-36) (n = 237), were analyzed using NN and conventional statistical tools. Kohonen networks identified three distinct clusters of IIQ scores. The three clusters represent the full spectrum of possible scores on the IIQ. We interpreted these clusters as reflecting good, moderate, and poor QoL. We estimated that a score of less than 50 on the IIQ would be representative of good QoL, between 50 and 70 would be moderate QoL, and greater than 70 would be indicative of poor QoL. Validation with the SF-36 data confirmed these categories. The present study demonstrated that the NN approach is opening new areas in the interpretation and clinical usefulness of QoL questionnaires. NN allowed the identification of three levels of QoL and should be useful in clinical decision making.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".