Bibliographic record
Abstract
New technology may be less invasive, may cause less collateral damage and faster recovery, and offers promise for better diagnostic and treatment modalities. Most new technologies when first distributed do not translate to better outcomes. The choice of a new technology should be based on a biological need and justified by an established physiological mechanism. Unfortunately, new technology is often developed for marketing advantage rather than to address a biological need. Food and Drug Administration is the main regulator for both pharmaceutical and medical devices in the United States; its goal is to establish safety and efficacy. The agency permits marketing of devices but does not actually approve them, and assigns risk as low (class 1), moderate (class 2), or high (class 3) (Table 1).TABLE 1: Food and Drug Administration Classification for Medical DevicesPreclinical mechanical testing is usually based on testing standards created by the American Society of Testing Materials and the International Standards Organization, which include static and dynamic testing to determine strength and fatigue properties of all components. For devices that allow motion, wear testing measures durability. Cell culture is often performed on wear material, and biological testing for spinal devices can include use of a laminectomy model by which wear debris is placed on the nerve and neurotoxicity is examined. This type of testing can lead to unexpected failure in vivo after approval. Testing of input parameters versus what actually happens in vivo is not understood, and correlation between testing standards and clinical results is often inadequate. Clinical trials are most important for confirming safety and efficacy. Study design is essential in choosing an appropriate control, whether nonoperative therapy or a competing device or technique. Time points appropriate for determining reasonable safety and efficacy must be selected. A randomized controlled trial is most appropriate when control efficacy is unknown, outcomes are poorly measured, cost of treatment is high, and acceptable alternatives are available. A randomized controlled trial yields evidence of the highest quality and can prove efficacy but cannot show efficiency of treatment. Efficiency involves broadened indications, greater surgeon variability, and oftentimes less than optimal training and inconsistent management before and after surgery. These problems can greatly influence the distributive nature of new technology. Examples of successful new technology for spine surgery include cervical disc arthroplasty performed to address a biological need, at least theoretically, to prevent adjacent segment degeneration. An excellent alternative is fusion. A less successful example is a minimally invasive shaver used in foraminotomy. This technique involves making a small laminotomy and passing a guidewire out the neuroforamen and back through the skin, allowing placement of a shaver that can be used to undercut the foramina and the subarticular area of the lamina; this is done under special neuromonitoring. This device was approved as an instrument rather than a medical device because it is removed at the time of surgery. Systematic problems, including poor indications, inadequate physician training, lack of regulatory oversight, faulty surgeon understanding of neuromonitoring principles, and unproven safety and effectiveness, may lead to complications. The final aspect of new technology is surgeon education. Many experienced surgeons see new technology as not very different from what they are already doing and may not adequately prepare for its use. Courses typically involve cadaveric or synthetic models and may not include critical components, such as use of neuromonitoring. Surgeons using new technology should gain experience by visiting proficient users or by having their cases proctored. Assisting at their own hospital or at another surgeon's institution may provide the best training. By using the instruments in a sham process, that is, by doing a familiar similar technique while using instruments needed to place the new device or apply the new technique, the surgeon can improve skills. Through minimally invasive placement of pedicle screws by an open procedure, the surgeon can use percutaneous instruments. Early cases should be proctored and critically reviewed by the surgeon; CT scans should confirm that desired results were achieved. Learning must be contemporary with performance of the surgical procedure; if time passes between undergoing training and doing the procedure, the surgeon should retrain to keep skills up-to-date. To overcome the learning curve, the surgeon usually will need to perform complex tasks, such as completing an entirely new procedure, in 30 to 35 cases, and will require as few as 10 cases to add a new step to a procedure. Nandyala et al1 reviewed minimally invasive transforaminal lumbar interbody fusion and found a significant decrease in time, estimated blood loss, and IV fluids after the first 30 cases but no differences in rates of complications. Park et al2 noted a 9% complication rate and reported that most complications occurred in the first 40 cases. Another concept involves mass learning versus distributed learning. Mass learning is achieved in a single session, such as a half-day program, whereas distributed learning takes place in 1 hour spread over 4 days. Distributed learning leads to significantly longer-term retention of technical skills.3 In summary, new technology offers the promise of improved clinical outcomes but must be based on biological plausibility and a biological need. The surgeon cannot assume that testing is rigorous, and should evaluate the literature himself before adopting a new technology. The surgeon's training must be completed by practicing with high-fidelity models or by visiting surgeons experienced in the technique.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".