Bibliographic record
Abstract
value cannot rule out the possibility that some area in the brain may be hypoxic or ischemic.We agree that this is a major limitation of the method.However, the same triticism could be brought against the measurement of cerebral oxygenation by methods that monitor a very limited cerebrd area, such as near-infrared spectroscopy (NIRS) or direct cerebral oxygen measurement with intracerebra1 probes.In practice, clinicians would benefit from simultaneous measurements (i.e., global and local) in patients with head injury.The aim of our study was to highlight the difficulties inherent in the interpretation of NIRS data in patients with head injury.We purposely studied conditions in which cerebral blood flow could reasonably be expected to vary in the Same direction in the territories that were simultaneously monitored using three methods: NIRS, transcranial Doppler ultrasonography, and jugular oxygen saturation.Among the multiple reasons that may explain the discrepancies that were observed and discussed in our study, the clinical setting is an important reason to consider.Indeed, measurement of light transmission by NJRS may he more difficult to perform in adults than in children or in head trauma with edema than during carotid clamping.A partition between intracranial and extracranial blood and also between arteriolar and venous compartments seems to be dependent on the different therapies that are used.It may explain why clinicians who work in different domains obtain NIRS data with different levels of accuracy and clinical relevance.Furthermore, improvement in NIRS technology and in the modeling of the light pathway through an adult skull should allow for the identification of the mechanisms that underlie the discrepancies that we observed between different monitoring techniques, and may find solutions that will correct for these discrepancies.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".