THE TURKISH VALIDITY AND RELIABILITY OF HOSPITAL FOR SPECIAL SURGERY-LUMBAR SPINE SURGERY EXPECTATIONS SURVEY
Bibliographic record
Abstract
Objective: The postoperative recovery expectations of patients are important for surgical decisions.The Hospital for Special Surgery-Lumbar Spine Surgery Expectations Survey (HSS-LSSES) is a questionnaire evaluating expectations from lumbar surgery.This study aims to adapt the HSS-LSSES to Turkish and to assess its validity and reliability. Materials and Methods:The methodology of this study was based on the COSMIN guideline.The Turkish version of the HSS-LSSES created with the double-back translation procedure and the Turkish version of the Quebec Back Pain Disability Scale (QBPDS) were administered to the participants who were scheduled for surgery with the diagnosis of lumbar radiculopathy, respectively.Cronbach's alpha coefficient and item analysis were used to assess internal consistency.Also, intraclass correlation coefficient (ICC) was used to determine test-retest reliability.Results: The study included 180 participants (54.4% male) with a mean age of 50.96±13.42years at scheduled lumbar spine surgery.HSS-LSSES had good internal consistency (Cronbach's α=0.87) and excellent test-retest reliability [ICC (2.1)=0.99;p<0.01].A strong negative correlation was found between HSS-LSSES-TR and QBPDS-TR (r=-0.71,p<0.01).It was observed that there was no ceiling and floor effect in the scale.Conclusion: HSS-LSSES-TR is a practical, valid, and reliable measurement method that can be used in clinical and research settings to evaluate the expectations of individuals planning for lumbar spine surgery and to examine how well these expectations are met after surgery.
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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 | 0.005 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".