The Short‐Form Inguinal Pain Questionnaire (sf‐IPQ): An Instrument for Rating Groin Pain After Inguinal Hernia Surgery in Daily Clinical Practice
Bibliographic record
Abstract
BACKGROUND: The Inguinal Pain Questionnaire (IPQ) is a standardised and validated instrument for assessing persisting pain after groin hernia surgery. The IPQ is often perceived as being too extensive for routine use. The aim of this study was to develop and evaluate a condensed version of the IPQ in order to facilitate its use in daily clinical practice. METHODS: The condensed form, i.e. Short-Form Inguinal Pain Questionnaire (sf-IPQ), comprises two main items taken from the IPQ. Four hundred patients were recruited from the Swedish Hernia Register and were sent the IPQ, sf-IPQ and the Short-Form McGill Pain Questionnaire (SF-MPQ) three years after hernia repair. Ratings from the IPQ and the sf-IPQ were converted to a 12-point scale. The reported scores for the two shared items in the IPQ and sf-IPQ were compared using the Intraclass Correlation Coefficient (ICC), Cohen's kappa and McNemar's test. RESULTS: After two reminders, the response rate was 69.8% (n = 279/400). The ICC for the IPQ and sf-IPQ scores was 0.78 (95% confidence interval 0.73-0.82, p < 0.001). Cohen's kappa was 0.66 (95% confidence interval 0.55-0.77, p < 0.001). The sf-IPQ systematically indicated a higher pain score than the IPQ (p = 0.013). CONCLUSIONS: Despite the systematic difference in level of pain scored, correlation, consistency and agreement were seen between the IPQ and sf-IPQ. The forms appear to be interchangeable, though the sf-IPQ may be a more sensitive instrument. The condensed structure of the sf-IPQ is more user-friendly and shows promise as a useful tool in daily clinical practice.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".