Intravenous Immunoglobulins (IVIG) in Chronic Inflammatory Demyelinating Polyneuropathy ( CIDP ): time to maximal recovery
Bibliographic record
Abstract
Background: The response of Chronic Inflammatory Demyelinating Polyneuropathy ( CIDP ) to Intravenous Immunoglobulins (IVIG) treatment is well established. However, determination whether patients who do not respond to 2 IVIG treatments or those whose condition stabilizes (ICE Trial) would benefit from additional treatments remains unclear. We aim to identify time period required to reach maximal strength gains from IVIG treatment (plateau). Furthermore, we will assess nerve conduction studies (NCS) changes over time with IVIG treatment. This will help in establishing a time course for treatment of CIDP with IVIG to maximize recovery. Methods: We performed a retrospective chart review of 27 patients with CIDP, with diagnosis confirmed by European Federation of Neurological Societies/Peripheral Nerve Society Guidelines (EFNS/PNS). Each patient’s strength response including: grip strength, knee extension, elbow flexion and dorsiflexion (using JAMAR Dynamometer) and NCS changes over time during IVIG treatment were analyzed. The primary outcome is duration of IVIG treatment, in months, required to reach a plateau in strength. Secondary outcome is NCS change including: Terminal Latencies, Conduction Velocities, Compound Sensory and Motor action potentials in nerves of upper and lower extremities over treatment time (emerging trends). Results: Pending (available by April 2015) Conclusion: Pending (available by April 2015)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".