Bibliographic record
Abstract
I read with interest David Lacomis and colleagues' editorial 4 on critical illness myopathy.I must take issue with their comments regarding the role of histopathological proof and the role of muscle biopsy.The causes for persistent weakness in a critically ill patient are multiple.The so-called "critical illness" myopathy most probably represents a spectrum of illnesses and not one entity.This continuum includes severe disuse (type II) atrophy, cancer-related necrotizing myopathy (paraneoplastic or those related to immunotherapy for malignant disorders), 3,5 and the spectrum of myopathies with heavyfilament (myosin) loss, from the focal loss of myosin seen in postparalysis paralysis syndrome 2,6 to a more severe, rapidly evolving myopathy with diffuse myosin loss. 1 Electrophysiologic studies may differentiate a neuropathy from a myopathy and may show muscle fiber irritability.More often, the electrophysiologic studies show a combination of neuropathic and myopathic features or are technically limited because of the problems inherent in performing electrodiagnostic studies in the intensive care units.Muscle enzyme elevation is not useful and may not always be seen.Muscle membrane inexcitability 7 is not specific to any one subset of this spectrum.Thus, an exact diagnosis cannot be ascertained without an open muscle biopsy.Muscle biopsy results impact treatment, and open muscle biopsy should therefore remain a mandatory part of the investigation of persistent weakness in critically ill patients.
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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".