Impact of Surgical Waiting-List Times on Scoliosis Surgery
Bibliographic record
Abstract
STUDY DESIGN: Survey. OBJECTIVE: The aim of this study was to evaluate the surgeon's perspective on the potential impact of prolonged surgical waitlists on the surgical care and perioperative management of patients with scoliosis. SUMMARY OF BACKGROUND DATA: The long waits for surgical treatment of scoliosis found in some countries may have serious implications for the complexity of surgery and perioperative care required if the curve progresses while waiting. The surgeon's perspective on this problem provides important information that needs to be taken into account during resource allocation. METHODS: Radiographs from 13 patients who had waited more than 6 months for scoliosis surgery were selected. Each patient had radiographs from the time of surgical booking and immediately preoperatively. The radiographs and a questionnaire were sent to 3 surgeons to canvass their surgical and postoperative plan. The surgeons were blinded to the fact that the radiographs were of the same patients at 2 time points. The patients' actual course of treatment was documented. RESULTS: Data for 11 patients were available for analysis. The average wait for surgery was 24 months (range, 17-30 mo). The mean curve progression was 25.3° while on the waitlist, from an average of 52° to 77°. By the time the patients had to undergo surgery, more anterior releases were added to posterior instrumentation alone in the surgical plan. Mean estimated operative time increased by 2.2 hours, mean estimated length of hospital stay increased by 1 day, and the estimated level of difficulty of surgery increased 2.33 grades. The predicted estimated blood loss also increased. CONCLUSION: From the surgeon's perspective, lengthy waitlists have a significant negative impact on the perioperative and postoperative care of patients with scoliosis by increasing the complexity of surgery. The actual course of treatment corresponded to the responses from these different surgeons. LEVEL OF EVIDENCE: N/A.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".