The Impact of Low-Cost Customized Cranioplasty Implants in a Low-Income Population: Does Quality of Life Improve?
Bibliographic record
Abstract
ABSTRACT Background: Individuals undergoing cranioplasty may encounter persistent functional impairments. Quality-of-life (QoL) assessment to address this is essential. This study aims to evaluate the long-term improvement in QoL after a cranioplasty at our center. Methods: In this observational, retrospective study, we assessed the QoL of patients who underwent cranioplasty and could be contacted by our research team. QoL was evaluated using EuroQol-5D-3L and SF-36 scales through phone interviews. We evaluated QoL changes at 3, 6 and 12 months. Friedman’s test and repeated measures ANOVA were used to assess QoL improvement through time. An exploratory analysis to search for possible modifiers of QoL improvement was conducted. Results: We included 28 patients with a median age of 30 (IQR 20−52) years, of whom 19 (79.2%) had a history of trauma. Twenty (71.4%) patients underwent cranioplasty with custom-made 3D-modeled implants. Long-term improvements in general QoL were observed in mobility, self-care, usual activities and pain/discomfort ( p < 0.001). Improvement in SF-36 scores showed significant mean differences in role limitations due to physical health (−32.14, 95% CI −50.37 to −13.91; p < 0.001), role limitations due to emotional problems (−21.43, 95% CI −38.5 to −4.35; p = 0.010) and pain (−9.65, 95% CI −16.36 to −2.93; p = 0.003). There were no significant modifiers of QoL improvement. Conclusion: This study showed promising results about QoL improvement experienced by patients with low-cost customized implants. Further research is necessary to preserve clinical and self-reported improvement and conduct patient-centered neurosurgical care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".