Definitions of the structure of psychological developmental periods: explanatory and descriptive potential in application to old age
Bibliographic record
Abstract
Spinal robotics have the potential to improve the consistency of outcomes in adult spinal deformity (ASD) surgery. The objective of this paper is to assess the accuracy of pedicle and S2 alar-iliac (S2AI) screws placed with robotic guidance in ASD patients. PubMed Central, Google Scholar, and an institutional library database were queried until May 2023. Articles were included if they described ASD correction via robotic guidance and pedicle and/or S2AI screw accuracy. Articles were excluded if they described pediatric/adolescent spinal deformity or included outcomes for both ASD and non-ASD patients without separating the data. Methodological quality was assessed using the Newcastle-Ottawa scale. Primary endpoints were pedicle screw accuracy based on the Gertzbein-Robbins Scale and self-reported accuracy percentages for S2AI screws. Data were extracted for patient demographics, operative details, and perioperative outcomes and assessed using descriptive statistics. Five studies comprising 138 patients were included (mean age 66.0 years; 85 females). A total of 1,508 screws were inserted using robotic assistance (51 S2AI screws). Two studies assessing pedicle screws reported clinically acceptable trajectory rates of 98.7% and 96.0%, respectively. Another study reported a pedicle screw accuracy rate of 95.5%. Three studies reported 100% accuracy across 51 total S2AI screws. Eight total complications and 4 reoperations were reported. Current evidence supports the application of robotics in ASD surgery as safe and effective for placement of both screw types. However, due to the paucity of data, a comprehensive assessment of its incremental benefit over other techniques cannot be made. Further work using expanded cohorts is merited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".