Bibliographic record
Abstract
We thank Stevens and Harper (1) for their response to our study and for identifying citations that help to highlight the important issues associated with research in this field. The aim of our study (2) was to determine the best standard nerve conduction study (NCS) marker of clinical carpal tunnel syndrome (CTS), in diabetic subjects, under the hypothesis that at least one parameter would reliably identify CTS. Surprisingly, the results indicated that standard NCS techniques fail to reliably distinguish the presence or absence of CTS in subjects with diabetes. From the results, we were able to infer that the electrophysiological changes at the wrist most likely arise from diffuse nerve injury associated with diabetes rather than signifying the specific symptomatic entrapment of the median nerve. Stevens and Harper express two main concerns with the study: the methods of electrophysiological evaluation and the potential for misclassification bias in clinical CTS cases. Stevens and Harper recommend and cite specific methods for which they suggest exclusion from the NCS evaluation invalidates our study results. We strongly feel, however, that the recommended methods either do not diverge significantly from our protocol or do not …
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 | 0.063 |
| 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 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.037 | 0.039 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".