Bibliographic record
Abstract
Reply: We thank Drs Venditolli, Lavigne, and Roy for their comments regarding our article.1 They have correctly pointed out the unit error we unfortunately failed to note in the editing process. The units should have been nmol/L. We agree it is difficult to compare much of the literature on serum ion levels because of the vast methods of collection, analysis, and units used. It may be prudent for those involved in this type of research to agree to a set unit of measurement for published material, therefore stopping the reader from having to make complex calculations to ensure correct comparisons. We also agree including the standard deviations from the mean would be useful and probably should have been included. However, we are uncertain high levels indicate increased risk of metal ion-related complications and are unaware of any evidence supporting this view. Some of our patients did start with higher than expected preoperative levels and these patients subsequently had some of the highest postoperative levels, but we have no evidence at 4 years that this is a cause for concern. However, with a small sample size, we must be careful not to extrapolate the data too far. A much larger prospective study incorporating, different head sizes, weights, gender, and metallurgy is required before we can safely answer questions like this. D. L. Back, FRCS Ed Guy's and St Thomas' Hospital Worcester, United Kingdom; D. A. Young, FAOrthA Melbourne Orthopaedic Group Melbourne, Australia; and A. Shimmin, FAOrthA Melbourne Orthopaedic Group Melbourne, Australia
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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.039 | 0.040 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".