Bibliographic record
Abstract
A current transformation within surgery is the development of minimally invasive techniques, particularly laparoscopy. Practice is required to reach competency in laparoscopic skills and, because the level of surgical skill is directly related to the outcome of the operation, highlights the importance of competence, which in turn is acquired through skill acquisition and retention. There is an advantage if skill acquisition is hastened and re-training time for surgeons is reduced. Laparoscopic surgery requires the surgeon to function with their vision occluded and observe their work on a monitor above the patient and off to one side. As depth perception is removed and instruments have a limited range of motion that means reduced degrees-of-freedom. Training laparoscopic techniques using a simulator such as the 3-Dmed® laparoscopic training device can shorten operating times, increase operative skills, and reduce the risk of complications. The objective of this study is to determine an optimal practice schedule for surgeons learning laparoscopic surgical techniques through a re-examination of the findings of both Shea & Morgan (1979) and Hynes-Dusel (2002) who found conflicting results when it came to the efficacy blocked as opposed to random practice as it relates to skill acquisition and retention. Research will assess random versus blocked training using a 3-Dmed® device. Participants will be taught three motor tasks under blocked (low contextual interference) or random (high contextual interference) sequence of presentation. Retention tests will be conducted after 10 min. (short-term retention) and 10 days (long-term retention).
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".