New surgical techniques and social media in orthopaedics. Is a scientific peer-reviewed journal assimilated to a social media platform?
Bibliographic record
Abstract
New techniques and modifications of old procedures appear and disappear in the history of Orthopaedics and Medicine.A brief summary of the articles published in history sections and topical collections are useful to understand the linkages between past and present [1][2][3][4][5].A brief review of a Journal published 20 or 30 years ago describes the trends and facts valid for that time.Are there influential papers from the 80s and 90s still state-of-the-art today?How did they get validated and are they really meaningful and used?How many procedures are really effective in improving patient's status of health or function?And if the procedures are effective, are some more effective than others and is the result related to the patient selection or to the surgeon's skills?Outcome studies analyze this with scales of evaluation that are more or less debatable; back in the early 90 s, adherents of quantitative and qualitative methods were arguing that their methodology was the only one correct.However, patients' related outcomes are driven by the surgeon's analysis or suggested during clinics.For years thereafter, economists and evaluators have been engaged in a passionate argument on randomized controlled trials (RCTs) vs. observational studies.In any case, evaluation should incorporate qualitative methods, be ethical, accurate and technically adequate, affordable/appropriate in terms of budget, and should be carried out by skilled persons in a timely fashion.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".