Parkinson's disease mild cognitive impairment classifications and neurobehavioral symptoms clarification letter
Bibliographic record
Abstract
We thank Ms. Horne et al. for the clarification of our misquoting of their paper (Wood et al., 2016). They clarify that 21% of their overall sample of patients with Parkinson's disease (PD-MCI) converted to dementia in over four years, which we erroneously attributed to the mild cognitive impairment (MCI) group in our discussion (McDermott et al., 2017). This was virtually identical to our overall conversion rate of 20%. Their conversion rate of patients with PD-MCI, as defined by two cognitive tests impaired (1.5 SD) within a single cognitive domain, was 51%, whereas the conversion rate was 38% when the PD-MCI group included patients with impairment within and between cognitive domains. Their conversion rates are similar to our rate of 42% (as defined with 1.5 SD impairment within or across domains) and the rate of 39% in a study with five-years of follow-up of incident cases (Pedersen et al., 2017). Our overall conversion occurred over a slightly shorter time span. In addition to conversion rates, all the studies acknowledge that some patients can revert to normal cognitive status, which varies based on classification criteria and length of follow-up. Comparable conversion across studies using similar criteria is reassuring and can encourage planning of targeted interventions (Hoogland et al., 2017).
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".