Bibliographic record
Abstract
Throughout history, prosthetic limbs have undergone significant changes in design and function. For example, an ancient bronze and iron prosthesis with a wooden core, discovered in Italy and dated back to 300 BC, has evolved into a modern shock-absorbing multi-axis prosthetic foot for walking on uneven ground. Recent advances in “neuro-controlled” prosthetics with microprocessor controllers further allow their users to produce smooth, multi-joint movements, simulating “real limbs”. With an increase in government funding focusing on researches in independent mobility, it is expected that new designs will improve immensely the quality of life of amputees. Are we approaching closer to the “ideal prosthetic limb”? The objective of this paper is to examine the evolution of various prosthetic designs and to re-apply some of the old concepts into new designs. The method used is mainly literature review. Results/conclusion: N/A. Wetz H, Gisbertz D. History of artificial limbs for the leg. Orthopade 2000; 29(12):1018-32. Pascual G. Amputations, walking and prosthesis development. An R Acad Nac Med (Madr) 2003; 120(3):593-607. Cottrell-Ikerd V, Ikerd F, Jenkins DW. The Syme’s amputation: a correlation of surgical technique and prosthetic management with an historical perspective 1994; 33(4):355-64.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".