Bibliographic record
Abstract
The percentage of cases in which minimally invasive surgical (MIS) techniques are used has increased and is expected to continue to climb. However, it is generally recognized that many MIS techniques can present a challenging environment for arthrodesis.1 Additionally, most biologics are more effectively used as graft extenders with local autologous bone. However, in MIS, often no bone needs to be “extended,” and no source of cells is known other than the limited amount of decorticated surface. All of these factors might play a role in the increased use of rhBMP-2 in these settings. In contrast, awareness of related complications is growing, and researchers are seeking safer options that will improve bone healing over inert implants such as traditional polyetheretherketone spacers. Total joint surgeons have known for decades what spine surgeons are rediscovering: that implant material and surface and structural characteristics can influence osseointegration and bone formation. Recent in vitro and in vivo basic science research is beginning to elucidate the cellular mechanisms involved in macro, micro, and even nanoscale architectural changes that can promote adhesion, osteoprogenitor differentiation, and osteoblast activity.2 Implants that allow physiologic degrees of tensile strain to be transferred to osteoblasts, those that have macroscale roughened surfaces that promote a stable bone/implant interface acutely, and those that have optimized surfaces with micro and upper nanoscale pits with specific architecture all have been shown to improve the production of biomarkers for bone formation. Early clinical data are promising. However, questions remain regarding how much autologous bone is needed for these devices, and whether the preclinical data will translate to better bone healing in human patients, particularly in challenging environments such as MIS.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".