Bibliographic record
Abstract
Exercise-induced hypoxemia [EIAH, arterial PO2 < 90 mmHg and/or alveolar-arterial oxygen partial pressure gradient (A-a DO2) ≥ 25 mmHg] occurs during strenuous exercise in some healthy women. There is conflicting opinion if performing successive bouts of strenuous exercise reduces the severity of EIAH. The aim was to (a) test the hypothesis that the severity of EIAH would be reduced with three successive bouts of strenuous exercise, (b) to determine if repeated bouts of exercise increases hyperventilation thus improving arterial PO2. Seven fit female subjects with EIAH [arterial PO2 or PaO2= 88 +/- 2 mmHg, A-a DO 2 = 25 +/- 3 mmHg and 7 fit female control subjects (PaO2 = 100 +/- 5 mmHg, A-a DO2 = 16 +/- 5 mmHg) performed three bouts of intense exercise on a cycle ergometer at 236 +/- 27 watts [oxygen consumption at end of each set = 48 +/- 6 mL/kg/min, or 96 +/- 5% of maximum] for 5 min each with 10 min of rest between sets. Arterial PO 2 increased [EIAH Delta = +4 +/- 5 mmHg. 95% CI = 0.6 to 7.8; Control Delta = +2 +/- 2 mmHg. 95% CI = 0.4 to 3.6] and arterial PCO 2 or PaCO2 decreased [EIAH Delta = -5 +/- 4 mm Hg, 95% CI = -7.4 to -2.2; Control Delta = -4 +/- 2 mmHg, 95% CI = -5.8 to -2.4] between set 1 and set 3 (P< 0.05). Also, 34% of the variance in the change in PaO2, was explained by the variance in the change of PaCO2 (P < 0.05). In conclusion, repeat exercise improves PaO2, which is related to improved hyperventilation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".