The Canadian Journal of CME / May 2009 41 QUICK QUERIES Topical Questions, Sound Answers Managing Diabetic
Bibliographic record
Abstract
yrig ht© Not for S ale o r Co mm erci al D istri buti on Una utho rise d us e pr ohib ited. Au thor ised user s ca n do wnlo ad, disp lay, view and prin t a s ingle cop y for pers ona l use Of the various end-organs affected by pro-longed dysglycemia, whether in the form of impaired glucose tolerance (IGT) or frank diabetes (Types 1 or 2), the peripheral nervous system (PNS) is particularly vulnerable.1 Thus, diabetic polyneuropathy (DPN) is a leading cause of morbidity and non-traumatic amputa-tions within this population. While risk factors for the development of DPN are established (Table 1), additional cryptogenic factors must influence the expression of neuropathic symp-toms. For example, while up to 50 % of all caus-es of peripheral neuropathy are related to dia-betes, in nearly one third of patients the exact etiology remains uncertain. Amongst these patients 40 % will manifest IGT.2 By contrast, Pirart et al.3 documented that 50 % of patients who had diabetes for 25 years had no evidence of DPN. Therefore, two distinct groups emerge, one ultra-sensitive to elevated blood sugar and the other relatively protected against it. Despite such wide variability in terms of time-to-onset once symptoms develop the pattern is charac-teristic. What are some clinical features? DPN is a slowly progressive, length-dependent neuropathy affecting primarily sensory fibers of both large (i.e., slowed nerve conduction, distal hypesthesia and reduced balance) and small (i.e., pain, autonomic dysfunction and reduced intra-epidermal nerve fiber density) caliber. Motor fibers are affected later in the course. Positive symptoms consist of tingling, thermal (burning and/or freezing) and electric dysesthe-sias. Negative symptoms consist of numbness, weakness and impaired proprioception. Recent evidence suggests that even in the “pre-diabetic ” state deficits in standing balance and trunk position sense are discernable.4
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.687 | 0.325 |
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".