The Use of a Self-Administered Questionnaire to Reduce Consultation Wait Times for Potential Elective Lumbar Spinal Surgical Candidates
Bibliographic record
Abstract
BACKGROUND: In a public health-care system, patients often experience lengthy wait times to see a spine surgeon for consultation, and most patients are found not to be surgical candidates, thereby prolonging the wait time for those who are. The aim of this study was to evaluate whether a self-administered 3-item questionnaire (3IQ) could reprioritize consultation appointments and reduce wait times for lumbar spinal surgical candidates. METHODS: This prospective, pragmatic, blinded, randomized controlled quality improvement study was conducted at a single Canadian academic health-care center. This study enrolled 227 consecutive eligible participants with an elective lumbar condition who were referred for consultation with a spine surgeon. All participants were mailed the 3IQ after their referral was received. Patients were randomized into the intervention group, in which leg-dominant pain reported on the 3IQ resulted in an upgrade in priority to be seen, or into the control group, in which no change to wait-list priority occurred. The main outcome measured was time to consultation for participants who were deemed surgical candidates following consultation. RESULTS: There were no significant differences between groups with regard to demographics, overall group wait times, proportion of surgical candidates, or disability. A total of 33 patients were deemed surgical candidates after consultation. The median wait from referral to consultation was shorter for the 16 surgical candidates in the intervention group (2.5 months; interquartile range [IQR]: 2.0 to 4.8 months) compared with the 17 surgical candidates in the control group (4.5 months; IQR: 3.4 to 6.9 months; p = 0.090). The odds of seeing a surgical candidate within the acceptable time frame of 3 months were 5.4 times greater (95% confidence interval: 1.2 to 24.5 times; p = 0.024) in the intervention group. CONCLUSIONS: The use of a simple, self-administered questionnaire to reprioritize referrals resulted in shorter consultation wait times for patients who required a surgical procedure and significantly increased the number of surgical candidates seen within the acceptable time frame. It may be valuable to consider adding the 3IQ to clinical care practices to better triage these patients on waiting lists.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".