Assessment of Acute Antidote Shortage and its Effect on Treatment Outcome
Bibliographic record
Abstract
Background: Antidotes play a vital role in the timely management of poisoning cases. Antidote shortage can be troublesome for healthcare staff, patients & their families. This eventually causes treatment delay, case complication and death. The purpose of this study was to assess the impact of antidote shortage on the treatment outcome of poisoned patients in various hospitals and pharmacies of Karachi. Methods: This is a cross sectional study in which 324 participants had responded to the study questionnaires which were approached by non-probability convenience technique from January 2015 to July 2015. The data was collected from the physicians and drug dispensers working in any public and private hospital or pharmacy or retail store of Karachi. The responses of physicians and drug dispensers about the common poisonous agents, commonly unavailable antidotes and associated issues were validated by patient prescriptions and by the responses of patient attendants. Results: The physicians and pharmacists both rated Pralidoxime, Flumazenil, snake antivenom, N-acetycysteine and naloxone as commonly unavailable antidotes. Similarly maximum number of antidote prescriptions received was of Pralidoxime (46%) followed by Flumazenil (35%). The acute shortage of antidote may lead to treatment delay as responded by more than 80% participants from each group. Conclusion: Both the hospitals & retail pharmacies have to keep all essential antidotes irrespective of their cost and must keep direct contact with antidote suppliers, so that the issues related to antidote unavailability can be minimized. There is a dire need of integrated health system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".